functions.py

from abc import ABC, abstractmethod

from math import sin, cos, e, atan2, atan, sqrt, pi, exp
import operator
import numpy as np


class ConstrainedFunction(ABC):

    def __init__(self, dimensions):
        self.D = dimensions

    @abstractmethod
    def evaluate(self, X):
        pass

    @abstractmethod
    def constraints(self):
        pass

    @abstractmethod
    def get_domain(self):
        """
        Return a list of tuple representing a domain
        of admissible individuals for each dimension
        """
        pass


class Rosenbrok(ConstrainedFunction):

    def __init__(self, dimensions):
        if dimensions != 2:
            raise ValueError(f"Dimensions must be equal to 2 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        """
        http://en.wikipedia.org/wiki/Rosenbrock_function
        """
        x = X[0]
        y = X[1]
        a = 1. - x
        b = y - x * x
        return a * a + b * b * 100.

    def constraints(self):
        """
        http://en.wikipedia.org/wiki/Test_functions_for_optimization
        """
        return {
            "g1":
                {
                    "name": "g1",
                    "func": lambda x: (x[0] - 1.) ** 3. - x[1] + 1.,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": lambda x: x[0] + x[1] - 2.,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-1.5, 1.5),
            (-0.5, 2.5)
        ]


class MishraBird(ConstrainedFunction):

    def __init__(self, dimensions):
        if dimensions != 2:
            raise ValueError(f"Dimensions must be equal to 2 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = X[0]
        y = X[1]
        a = 1. - cos(x)
        b = 1. - sin(y)
        c = x - y
        return sin(y) * e ** (a * a) + cos(x) * e ** (b * b) + c * c

    def constraints(self):
        """
        http://en.wikipedia.org/wiki/Test_functions_for_optimization
        """
        return {
            "g1":
                {
                    "name": "g1",
                    "func": lambda x: (x[0] + 5) ** 2 + (x[1] + 5) ** 2,
                    "op": operator.lt,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-10, 0),
            (-6.5, 0)
        ]


class Townsend(ConstrainedFunction):

    def __init__(self, dimensions):
        if dimensions != 2:
            raise ValueError(f"Dimensions must be equal to 2 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = X[0]
        y = X[1]
        a = (x - 0.1) * y
        b = cos(a) ** 2
        c = 3 * x + y
        d = x * sin(c)
        return (b * (-1.)) - d

    def constraints(self):
        def const1(X):
            x = X[0]
            y = X[1]
            t = atan2(x, y)
            a = (2 * cos(t)) - (0.5 * cos(2 * t)) - (0.25 * cos(3 * t)) - (0.125 * cos(4 * t))
            return a ** 2 + (2 * sin(t)) ** 2

        return {
            "g1":
                {
                    "name": "g1",
                    "func": const1,
                    "op": operator.gt,
                    "target": lambda x: x[0] ** 2 + x[1] ** 2
                }
        }

    def get_domain(self):
        return [
            (-2.25, 2.5),
            (-2.5, 1.75)
        ]


# TODO: Crea numeri complessi?
class Simionescu(ConstrainedFunction):

    def __init__(self, dimensions):
        if dimensions != 2:
            raise ValueError(f"Dimensions must be equal to 2 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = X[0]
        y = X[1]
        return 0.1 * x * y

    def constraints(self):
        def const1(X):
            x = X[0]
            y = X[1]
            rt = 1.
            rs = 0.2
            n = 8
            a = n * atan(x / y)
            b = rt + rs * a
            return b ** b

        return {
            "g1":
                {
                    "name": "g1",
                    "func": const1,
                    "op": operator.ge,
                    "target": lambda x: x[0] ** 2 + x[1] ** 2
                }
        }

    def get_domain(self):
        return [
            (-1.25, 1.25),
            (-1.25, 1.25)
        ]


# ====================================================================================================
# Some functions implemented from CEC 2010 Competition on Constrained Real- Parameter Optimization
# Reference: http://www3.ntu.edu.sg/home/epnsugan/index_files/CEC10-Const/TR-April-2010.pdf
# ====================================================================================================


class C01(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [0.030858718087483, -0.078632292353156, 0.048651146638038, -0.069089831066354, -0.087918542941928,
             0.088982639811141, 0.074143235639847, -0.086527593580149, -0.020616531903907, 0.055586106499231,
             0.059285954883598, -0.040671485554685, -0.087399911887693, -0.01842585125741, -0.005184912793062,
             -0.039892037937026, 0.036509229387458, 0.026046414854433, -0.067133862936029, 0.082780189144943,
             -0.049336722577062, 0.018503188080959, 0.051610619131255, 0.018613117768432, 0.093448598181657,
             -0.071208840780873, -0.036535677894572, -0.03126128526933, 0.099243805247963, 0.053872445945574])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        a = np.sum([cos(_z) ** 4 for _z in z])
        b = 2 * np.prod([cos(_z) ** 2 for _z in z])
        c = sqrt(np.sum([i * _z ** 2 for i, _z in enumerate(z)]))
        return -1. * abs((a - b) / c)

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return 0.75 - np.prod(z)

        def g2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum(z) - 7.5 * self.D

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (0, 10) for _ in range(0, self.D)
        ]


class C02(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-0.066939099286697, 0.470966419894494, -0.490528349401176, -0.312203454689423, -0.124759576300523,
             -0.247823908806285, -0.448077079941866, 0.326494954650117, 0.493435908752668, 0.061699778818925,
             -0.30251101183711, -0.274045146932175, -0.432969960330318, 0.062239193145781, -0.188163731545079,
             -0.100709842052095, -0.333528971180922, -0.496627672944882, -0.288650116941944, 0.435648113198148,
             -0.348261107144255, 0.456550427329479, -0.286843419772511, 0.145639015401174, -0.038656025783381,
             0.333291935226012, -0.293687524888766, -0.347859473554797, -0.089300971656411, 0.142027393193559])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return np.max(z)

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            a = np.sum([_z ** 2 - 10 * cos(2 * pi * _z) + 10 for _z in z])
            return 10 - (1 / self.D) * a

        def g2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            a = np.sum([_z ** 2 - 10 * cos(2 * pi * _z) + 10 for _z in z])
            return (1 / self.D) * a - 15

        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            # element-wise operation
            y = z - 0.5
            a = np.sum([_y ** 2 - 10 * cos(2 * pi * _y) + 10 for _y in y])
            return (1 / self.D) * a - 20

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-5.12, 5.12) for _ in range(0, self.D)
        ]


class C03(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [111.17633500088529, 92.07880492633424, 417.9818592609036, 253.16188128024302, 363.5279986597767,
             314.334093889305, 187.32739056163342, 240.4363027535162, 422.60090880560665, 327.63042902581515,
             62.04762897064405, 25.435663968682125, 360.56773191905114, 154.9226721156832, 33.161292034425806,
             177.8091733067186, 262.58198940407755, 436.9800562237075, 476.6400624069227, 331.2167787340325,
             75.205948242522, 484.33624811710115, 258.4696246506982, 419.8919566566751, 357.51468895930395,
             166.3771729386268, 47.59455935830133, 188.20606700809785, 184.7964918401363, 267.9201349178807])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([(z[i] - z[i + 1]) ** 2 for i in range(0, len(z) - 1)])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-1000, 1000) for _ in range(0, self.D)
        ]


class C04(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [0.820202353727904, 5.260154140335203, -1.694610371739177, -5.589298730330406, -0.141736605495543,
             9.454675508078164, 8.795744608532939, 9.687346331423548, -3.246522827444976, 6.647399971577617,
             1.434490229836026, -0.506531215086801, 0.558594225280784, 7.919942423520642, 1.383716002673571,
             -1.520153615528276, -2.266737465474915, 6.48052999726508, -8.893207968949003, -3.528743044935322,
             6.063486037065154, -4.51585211274229, 7.320477892009357, -8.990263774675665, 9.446412007392851,
             -6.41068985463494, -9.135251626491991, 2.07763837492787, 8.051026378030816, -1.002691032064544])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return np.max(z)

    def constraints(self):
        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return (1 / self.D) * np.sum([_z * cos(sqrt(abs(_z))) for _z in z])

        def h2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([(z[i] - z[i + 1]) ** 2 for i in range(0, int(self.D / 2 + 1))])

        def h3(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([(z[i] ** 2 - z[i + 1]) ** 2 for i in range(int(self.D / 2 + 1), self.D - 1)])

        def h4(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum(z)

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "h2":
                {
                    "name": "h2",
                    "func": h2,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "h3":
                {
                    "name": "h3",
                    "func": h3,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "h4":
                {
                    "name": "h4",
                    "func": h4,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-50, 50) for _ in range(0, self.D)
        ]


class C05(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array([72.10900225247575, 9.007673762322495, 51.86632637302316, 41.365704820161, 93.18768763916974,
                           74.53341902482204, 63.745479932407655, 7.496986033468282, 56.16729598807964,
                           17.71630810614085,
                           28.009655663065143, 29.36357615570272, 26.966653374740996, 6.892189514516317,
                           44.29071160734624,
                           84.35803966449319, 81.16906730972529, 92.76919270133271, 3.826058034047476,
                           7.231864548985054,
                           14.446069444832405, 46.49943418775763, 22.155722253817412, 69.11723738661682,
                           88.99628570349459,
                           58.74823912291344, 52.265369214509846, 47.030120955005074, 53.23321779503931,
                           5.778976086909701])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return np.max(z)

    def constraints(self):
        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return (1 / self.D) * np.sum([-1. * _z * sin(sqrt(abs(_z))) for _z in z])

        def h2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return (1 / self.D) * np.sum([-1. * _z * cos(0.5 * sqrt(abs(_z))) for _z in z])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "h2":
                {
                    "name": "h2",
                    "func": h2,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-600, 600) for _ in range(0, self.D)
        ]


class C06(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-1.595515627742907, 7.633467047559741, -5.764100483472472, 8.103197246263033, -0.059515969417191,
             -0.737189363693078, -9.190862358160823, 4.22087353933443, -1.745435308213725, 9.499044614342985,
             3.82068618551277, 2.569334886907409, 9.354368119489862, -0.852114934846258, 4.714177466874696,
             6.775420647884232, -9.074204717422479, -3.760650327490145, -0.77805530989772, -7.487007842931314,
             4.435061566086135, -6.952711886757461, -8.752326993212105, -2.411334215593357, -6.149894283287328,
             1.049303005795593, -6.049093253116644, 0.950328133373404, 1.443084229017085, -0.163829799788475])

        if dimensions == 10:
            self.M = np.array([[-0.427907650939441, 0.33300277992222, 0.158498600541243, 0.278765719516325,
                            0.222299060911893, -0.29474502069992, 0.370057970680927, -0.501453646373309,
                            -0.280145449898877, -0.000651265117346], [
                               0.224165315142979, -0.261710409707008, 0.461868247797853, 0.088426027905637,
                               0.181816871315433, 0.373896699539441, 0.129585371011854, -0.381595698048136,
                               0.299800543352535, 0.48473910987024], [
                               0.53537800271001, -0.346919032183315, -0.245187528106815, 0.616142955927466,
                               0.041841873460541, -0.159575037377286, 0.105947745849249, -0.048303985087219,
                               -0.328079281746379, -0.069709134117085], [
                               -0.416970506616096, -0.38848171660274, -0.094221759803838, -0.004397275883214,
                               -0.237544237428415, -0.087984765871128, 0.123623297192573, 0.287283813863898,
                               -0.330878858096249, 0.62837597392732], [
                               -0.173113815964007, -0.41172550558533, -0.499430088608083, -0.219823423370544,
                               -0.134153464975979, 0.107615420480369, 0.438598883104602, -0.36754189637868,
                               0.291862561306911, -0.24605477533745], [
                               -0.238828717007495, -0.377132503962298, -0.036565581442029, -0.115315158370499,
                               0.298745579814478, 0.119889740767446, -0.652016881734357, -0.358800646641909,
                               -0.326697807053803, -0.147917018157104], [
                               -0.430957521699839, -0.225924726595994, 0.119112314369716, 0.573035485004016,
                               0.085368416951095, -0.090379611695263, -0.158290455997733, 0.252439376164776,
                               0.529634351567821, -0.189541361218442], [
                               0.038944335110613, 0.082059986739041, -0.407974613233548, -0.116148517661389,
                               0.807124465614906, -0.104709855101281, 0.075051783295566, 0.217165356560047,
                               0.152079302286292, 0.271071947907043], [
                               0.158807462159919, -0.014816097582468, -0.081504338504322, -0.114995329704289,
                               -0.203139388356386, -0.726380618422525, -0.310128975831262, -0.313282470241953,
                               0.332760870420427, 0.284154344591495], [
                               -0.081746794746219, 0.423796877332073, -0.504499564436954, 0.34367868531852,
                               -0.228250720437377, 0.409151634256662, -0.27457871527628, -0.212617996823112,
                               0.090750526203861, 0.304532143452476]])

        if dimensions == 30:
            self.M = np.array(
                [0.023071358604305, -0.243539203031734, 0.187927245392782, 0.104249328959388, 0.260716866807433,
                 0.221583385806611, -0.252797010046177, 0.100936946167369, -0.211370612329087, 0.202097745021358,
                 -0.109876100185841, 0.145097436798732, 0.085948612459317, -0.146422297038938, 0.190867385282501,
                 0.20021353815904, -0.216595920201605, 0.020808284235714, 0.116861967544113, 0.125705173410299,
                 -0.12742261831595, 0.154492340007543, -0.02487562612683, -0.130654692719704, 0.041242046151542,
                 0.137578302481707, -0.113685553667173, 0.080918468352169, 0.231134631372462, -0.500691454466717,
                 0.219813700073054, 0.281621212959571, -0.139807465673278, -0.110419741705267, 0.111375560917958,
                 0.18950250606953, -0.197263817723919, -0.120573644625486, 0.150326895316473, 0.181439845450315,
                 -0.160383535661352, 0.119334369356968, 0.083421908456657, 0.205356036944096, 0.070741886265028,
                 -0.010306823979388, 0.165894298461859, 0.286426975125101, 0.242550434036209, -0.257446539575199,
                 -0.124841748949664, -0.284707849202713, -0.266045211188684, 0.149407650859431, 0.234265400495501,
                 0.173236453713421, -0.268903873738916, -0.039222449976035, -0.076974292905734, 0.007845772247666,
                 -0.004556210660988, -0.185010374871545, 0.301301193684884, -0.10921707132215, 0.056649154772895,
                 -0.319259584148745, -0.121951562968817, -0.114946550830203, 0.299113248329728, 0.245003405745115,
                 0.095158583529753, -0.055656948570354, -0.015031072793883, 0.179092320602946, -0.139122690964128,
                 0.015458176172072, -0.473892415411379, 0.248799402354716, 0.082952673098456, 0.113131317862851,
                 -0.149011135793801, 0.130435444369303, -0.13561458613853, 0.11308864609885, 0.036204321234517,
                 0.087604692517882, 0.120848540211163, 0.19940636435041, -0.036809747154144, 0.273890394537969,
                 0.084637445870772, 0.010234637261837, 0.0163505679041, -0.291324368670689, 0.139923837626945,
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                 -0.159759583212716, -0.030595452046069, -0.260893173081313, 0.174126057613149, -0.184055361204547,
                 -0.454982071202166, 0.081028739628757, -0.15454679842635, -0.245309575415441, 0.070738655949495,
                 0.114407183940778, -0.000803285383987, -0.029585292203112, 0.034841728346049, -0.090378694457875,
                 -0.087253833015541, 0.201658706322267, 0.107556168250323, 0.538785527750462, 0.027360507247175,
                 0.068052364992373, -0.162699590061949, -0.027702982041988, -0.062665317946852, 0.088925544163789,
                 0.287078549836071, -0.026926010195244, 0.059356754643412, -0.389446957988569, 0.022367979523514,
                 0.192399044103956, 0.050439485449618, 0.023364347605351, 0.017553373460626, -0.013236479997603])
            self.M = np.reshape(self.M, (30, 30))

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        if dimensions != 10 and dimensions != 30:
            raise ValueError(f"Dimensions must be 10 or 30 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return np.max(z)

    def constraints(self):
        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            y = self.M.dot((x + 483.6106156535 - self.o[:self.D])) - 483.6106156535
            return (1 / self.D) * np.sum([-1. * _y * sin(sqrt(abs(_y))) for _y in y])

        def h2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            y = self.M.dot((x + 483.6106156535 - self.o[:self.D])) - 483.6106156535
            return (1 / self.D) * np.sum([-1. * _y * cos(0.5 * sqrt(abs(_y))) for _y in y])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "h2":
                {
                    "name": "h2",
                    "func": h2,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-600, 600) for _ in range(0, self.D)
        ]


class C07(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-1.46823271282738, 47.51401860909492, -30.421056514069576, -7.707941671844303, -21.74698421666629,
             -17.88116387879569, 5.274442455807971, 18.71403753778708, -36.959734507345146, -20.72950462154263,
             25.4701966548936, -25.439992885801573, 1.054563129830697, -31.556579857545657, -19.320382777005047,
             17.16774285348282, 34.66536814401755, -31.803705714749462, -12.926898387712775, 25.489686517508602,
             -45.23000430753644, 36.31774710581284, -18.38690515559357, 34.86816378160691, -37.530671214167334,
             19.288852618585977, 0.684612418754519, -12.636795982748637, 15.005454148879409, -40.468678588994315])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x + 1 - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            y = x - self.o[:self.D]
            a = exp(-0.1 * sqrt(1 / self.D * np.sum(_y * _y for _y in y)))
            b = exp(1 / self.D * np.sum(cos(0.1 * _y) for _y in y))
            return 0.5 - a - 3 * b + exp(1)

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-140, 140) for _ in range(0, self.D)
        ]


class C08(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-1.46823271282738, 47.51401860909492, -30.421056514069576, -7.707941671844303, -21.74698421666629,
             -17.88116387879569, 5.274442455807971, 18.71403753778708, -36.959734507345146, -20.72950462154263,
             25.4701966548936, -25.439992885801573, 1.054563129830697, -31.556579857545657, -19.320382777005047,
             17.16774285348282, 34.66536814401755, -31.803705714749462, -12.926898387712775, 25.489686517508602,
             -45.23000430753644, 36.31774710581284, -18.38690515559357, 34.86816378160691, -37.530671214167334,
             19.288852618585977, 0.684612418754519, -12.636795982748637, 15.005454148879409, -40.468678588994315])

        if dimensions == 10:
            self.M = np.array([[-18.785768809450733, 33.616954860437176, 26.882113915382682, -10.43306419742936,
                                0.944892894082476, -3.353896433259691, 3.535212733907637, 7.394276941396782,
                                7.790908752641235, 2.091292109983567], [
                                   -0.38080580880289, 10.420967284673154, 9.347291913115678, -20.926490513724943,
                                   11.4259031588907, 1.10563725749954, 36.87928250597037, -1.910339780463472,
                                   7.56116849320932, -9.743066435789972], [
                                   -12.341688082549489, 6.362199355273804, 8.248429939792464, 8.089256366417835,
                                   0.069234506619011, 2.578624135957486, -0.497340218618186, -2.062722095484327,
                                   1.430205147745766, 15.52200376094467], [
                                   -17.00654251044467, -12.679306064055677, 51.65812051951116, -3.97661207806369,
                                   3.934938475013658, -30.777202564613845, 6.146597147627116, -11.404959107806402,
                                   12.694206030880832, -9.395143228117439], [
                                   -5.484749154282662, -13.643476981518553, -20.8125786035429, 12.48063177681885,
                                   0.844978205995699, 24.830393330514045, 33.83850518570256, -17.003569707093064,
                                   -5.293964367404844, 26.065703095424336], [
                                   11.422878520470586, 10.221461943150496, -5.999478987400263, -8.935891602574142,
                                   3.340791625151446, 3.924548854255449, -6.760571785727851, 14.016300477765046,
                                   2.353396935795215, -15.957358828479556], [
                                   14.1069797350053, -0.689797572922294, 25.92835826668488, -30.138271725378775,
                                   12.953067028884863, -17.125782201118525, 19.122903237509483, 3.85021017121606,
                                   14.44987126033593, -37.768641488073015], [
                                   1.817162027385162, -4.52289774299815, 2.596064824368431, -3.077970366333548,
                                   3.666238380627702, -3.142271967105208, -1.93910379576585, -1.132846020949443,
                                   -1.459397119228072, -4.385065305078107], [
                                   17.05963501513677, -40.88734304067851, -9.04136854737176, 9.207816613351653,
                                   2.483559081696921, -3.135238286666343, -0.515970843448524, -10.448164970351954,
                                   -3.97908386413912, -5.010151763870892], [
                                   -2.100410473384162, 4.285743492212902, 18.13880373052391, -5.569156622351854,
                                   0.020414928764168, -5.531568307180828, 1.750746232557792, 2.018382353850689,
                                   8.96737078655512, -3.593654241962948]])

        if dimensions == 30:
            self.M = np.array(
                [2.511190264603399, 8.257741094092125, -4.576972024334265, 11.663051871013478, 1.839271199160904,
                 -3.930477981638431, 6.381869129516557, 5.554163663846647, 3.792259995720394, -8.119579509114208,
                 -6.749873116676933, -4.16744839268803, -5.247229107097693, 4.380091021522936, 1.636265206648646,
                 1.545744455341335, 6.729651169519672, -10.841704025647012, 5.27133366266056, -7.315142457952093,
                 5.586455189183544, 0.134360209805352, -4.757951297029205, -7.494064226166734, -5.71422876353246,
                 -3.494974588660678, 16.86892010541546, -6.058600435639238, -11.353403083342686, -1.092357620889664,
                 -1.176252081542598, 0.194672319059377, 9.262321627779777, -4.877620455960017, -3.013825305151295,
                 4.138941347418219, -4.381399078195643, -1.095599959738453, 3.362553950833766, -2.415178728944297,
                 -6.861613142354518, -0.005940467288075, 2.135933445957106, -0.307275749657439, 9.502954614655595,
                 3.789220991977143, -2.76964275770075, 2.672716708411572, -3.374116812054481, -2.041106775198442,
                 -3.340247070143979, -0.837499094352104, -5.259049047845686, -1.521493877085425, 3.560245065816806,
                 0.587849049777596, -8.24965720380312, 1.577881184618359, 2.56392435402041, 1.537958604481863,
                 -1.308581228315848, -2.050371351098398, -3.874959824774145, -2.136018990593701, 0.425374281070453,
                 3.043654044666869, 2.852463840006697, -4.609692730685587, -2.676989994934014, 0.018654380314467,
                 4.1843245155883, -2.941454921101105, -9.0702547942116, -2.890167502434176, -6.517087530456076,
                 -6.458220473167814, 5.214757843970958, -5.672828398157034, -1.291826775778004, -0.425237758250728,
                 2.004256913177237, 7.010207042020759, 3.245852998964355, 0.975852195193769, 1.406362603904871,
                 -2.052462316198574, 2.872078581244381, 2.445851054572349, 1.108924529459803, -0.260629199129536,
                 -0.691301322822554, -1.994532314862069, -8.604754485993125, -2.97610726321536, -2.546281717050659,
                 0.13089272819699, -2.095263510257371, 3.356968437392792, 0.259978867749323, 5.401823223841656,
                 3.061295803499265, 1.403978462555566, 3.459625904755164, 0.875144781306571, -4.037867043038509,
                 2.69814135487501, -1.046480563223284, -0.162120818498023, 3.972120681919849, -0.360095886115473,
                 -3.590431022975543, 1.135744786203014, 6.085551336331358, -0.306597750598646, -2.349583036995845,
                 3.903939972739528, -1.48181865824383, -2.878395159465752, 7.258503538363769, 4.796644771310021,
                 -0.444232081955838, -3.571385678786192, 13.286045625307453, -12.839633153682575, -4.489679872696604,
                 7.869013641543565, -1.965105477391064, -8.636636350365924, -0.094822649845242, -10.673493697992319,
                 -12.53156986402604, -1.854979185138595, -4.845831775303889, -3.303193299843544, 14.504105531464209,
                 2.590044768433951, -6.135686158996087, -4.270904315841696, -6.861683086853831, -2.744870140116196,
                 -5.102676391316569, 5.943125003510186, -8.766966677691988, 2.000662359536807, 0.48220557102434,
                 7.450887387478847, -10.45013256174983, 3.625933262996068, 4.179177543735097, 3.98247679713907,
                 -10.464299208072154, -0.185820260985436, 6.552041396672776, -5.911945191669922, 8.168937620030086,
                 -10.982208830632558, -3.00135846456146, 2.912065997677753, 2.671937505436929, -3.256558888247419,
                 -1.010981945614573, -8.18916926806801, -12.001606012082647, 0.565140546704374, -6.816130902986662,
                 7.034212706943163, 12.40601107576485, -14.537675875626526, 1.401546347401862, 0.070761477781216,
                 5.173018588042309, 17.500331178638167, 3.097673254889692, -4.026635320023784, 3.007819170833911,
                 -6.986787465181084, 6.394195915881141, 3.687826212758699, -10.500866576776128, -13.151879796730649,
                 -5.781123825040053, -4.695427919915561, 9.384139425829048, -7.575211154097536, 8.514278328525183,
                 -5.720630545110835, -3.828493595935516, 5.90998861394398, 0.148907769173485, -0.631185579095166,
                 -7.572076478194538, -3.770557393047349, -8.178577514209056, -5.098256869553833, -12.07105725371895,
                 1.248737932444238, 0.478544192523777, -8.678825875969533, -0.946209858630089, -0.78972756845699,
                 -8.64978321329506, 2.485419664365408, 12.153488342644684, -3.062242328783848, -0.170370275829816,
                 -2.093524789628705, -5.15404262324049, 4.508277504607992, 1.61064781893081, -3.274879887486953,
                 2.268818310206689, 7.300452401745146, -4.331575628327129, 10.74428874720899, 0.15049273777556,
                 -0.522703831910356, 6.686183057924437, 3.782219973575128, 2.188704155720839, -8.966284106856413,
                 7.937092834912006, -1.31567415453634, -3.395220022143096, 1.559847175500902, 2.78883479224772,
                 -2.138994752636062, 8.323858134206022, 1.103349691459627, 2.080995858068952, 3.671907915458957,
                 10.73018786831511, -0.786574921144261, -12.077147983799486, -3.200483300835418, 1.964680859010843,
                 -2.657000417145831, 12.549412268552729, -5.048053145262655, -8.92560318109584, -3.291078633192861,
                 0.041055194191982, 4.198164286592344, 0.380892004577647, -8.23942251831774, 4.779957612796264,
                 -3.478785140983899, -4.340229693544734, -1.26961685769858, 6.146810236016467, -3.071864805992567,
                 0.971337912956829, -5.896120164000506, -0.757550831858382, 0.878200934289778, -1.532518943062243,
                 5.844371522790389, -0.837014728066397, -3.907278737140878, -2.030173016623314, 3.202676786182734,
                 1.470137096717735, 6.858775309836105, -5.751687072684216, 2.261109122092457, 5.446233047509159,
                 -0.994575214031629, -4.198066474981203, -0.916283719062444, -4.341766414322003, 0.660003095340524,
                 -4.758805234887069, -1.249096368305273, 5.230241683696193, -19.44236789411332, 3.990165558570265,
                 -5.21678532927235, -4.960118855176376, 2.113664727176411, 0.365246740004055, 3.670941659701974,
                 -1.914288039871711, -6.094333718753953, -2.682967627659987, 1.227422326532798, -9.433645672535167,
                 4.96643787530529, -3.616907241245708, -0.145552345328748, -2.280394788413511, 2.265409089297554,
                 -5.317818474692691, 4.356958717932082, 9.75139488271248, 2.184418024240871, 4.551729219908339,
                 -3.203937519303825, -8.161877539527412, 9.881964507838315, -0.942679393999977, -4.09075977260806,
                 5.895714456877837, 5.673958378336709, -7.551189095277389, 8.6763663921392, -8.74212847286295,
                 7.876941413449155, 6.186314942878492, -1.385292259378321, 2.339350501494244, -7.303402814099851,
                 -0.620345906721456, -3.21480044327725, -0.528558678306147, -0.327651725159052, 6.60017678920534,
                 -4.030480514390237, -2.74819741682516, 1.131719959794813, 4.041615304275, -3.807276819902737,
                 7.174795405973731, -7.105910692661075, -16.16824790535485, -8.490742807482299, -1.205275569694925,
                 -3.213783451897369, 10.19460083362542, -8.1537702193513, -3.34614228867147, 6.852576635102674,
                 5.050755100515472, -1.015084166538332, -4.488951762662663, 3.071909362047136, -6.309214790805194,
                 4.607171586689359, 0.518771985931275, 2.576207604994254, -2.538951540887637, 3.871170428002043,
                 0.86099787829018, 9.333192517131101, 3.898241746958841, 2.045637143082407, -4.632172666627585,
                 -3.251052343878506, -10.340844584872428, 9.938362935470167, 2.681875064667896, -0.30928871960663,
                 -3.553804690623363, -11.909220170048043, 3.407795649838816, -0.852234992909218, 1.470543694236073,
                 0.018651383236502, -0.400939937078999, 1.006604199340888, 5.546567833598792, 8.936287451220219,
                 -2.296736642073308, -7.507685960269931, 3.878804185286892, 1.927870783876082, 8.606052457582582,
                 -4.902250427177561, -0.369415251862622, 3.771105898431047, -4.497236148553519, 6.92064424996612,
                 6.555606653106257, 3.979481264635631, 0.551670017289274, -1.250380372234189, -11.577926205347564,
                 0.98949362466787, 4.643449205576342, 0.039903009834915, 2.717889467266028, 2.628829050189848,
                 -1.391097858187011, 2.040534828877561, 14.571897255676616, 1.64579970252636, -0.601128848469179,
                 -0.840404796582696, -1.463197992681945, 3.202376802222665, -1.671176838446732, -3.3759766251534,
                 -3.937515676606634, -3.139929919295693, 7.06320923587761, 1.809248475068868, 8.870247836692679,
                 -7.116279757514643, -0.561569800957993, 1.607623766047816, -0.442980337701897, -5.392629763518957,
                 3.253312934476978, -0.53910491395161, -7.500926900397016, 0.980573085142957, -5.634247758036995,
                 2.236908157606175, 3.147822704551946, -5.937762475731414, 0.887239059660158, 3.778662703078195,
                 4.579851636624116, 9.68224407023739, 0.005007990083924, -2.066608587954049, -2.840100747113819,
                 1.07267047086035, 6.107120400203828, -2.014818439278602, -7.165823950693442, -9.272074819149603,
                 -2.680240010855875, 0.356004349959002, -0.414456907621788, -0.067764618278143, 5.320643303727243,
                 0.737875437883548, 1.213668435898487, -3.145814327985767, -2.507697183673514, 2.536104038824088,
                 3.558431317438912, 2.131159153330951, 3.764201252276733, 0.816204397371172, -4.632956423959657,
                 0.184120289127522, 1.217433374070159, -4.036068485529014, 2.258556949877698, -0.910082542366675,
                 3.754187256312381, 7.457650756177197, 1.456847256376522, -1.608612250250221, -0.757648771963665,
                 3.790322347324032, 0.887040099051261, -6.027791111032598, -0.328191702726934, 0.866002766609515,
                 6.578208237675698, 0.818410293291321, -9.288863917296439, 22.14413550757907, -5.223269252930489,
                 1.804141126348005, 6.924767448007314, -2.776780120617464, -2.494384185627731, 4.743636200841757,
                 2.69082215234061, 6.088855334101281, 0.981039450131136, -0.123672950299567, -0.140131828740133,
                 -13.797914058713273, -1.914030716314594, 1.35605332601434, 2.48584096184779, -2.669304324669153,
                 2.289369793279759, -12.59957022859263, 0.93847348962723, -3.000750651813473, -6.748464919761018,
                 -2.998991813272391, 7.50927404965129, -4.712789813740673, 2.583974262309686, 4.259407321635679,
                 -4.302432019312597, 1.030219160973822, -0.035401401034044, -3.864603659100286, 11.181713062447539,
                 1.799128842154124, -4.632382202673897, -2.780991517575134, -2.531316968742212, 10.165545659183975,
                 1.550622641761506, 5.402782921171298, 7.066734336942432, 3.559465053224369, -7.838510786198628,
                 0.872794527931054, -6.729399256798964, 1.851943659950421, -3.17302307338725, 0.303477456308984,
                 -7.294328632117977, 0.431243357642238, 12.108553118909567, 2.785512313731942, 3.951422589853138,
                 5.216571541524427, -14.01807799640241, 3.680618250481408, 10.312528175880152, 9.072426201364811,
                 -5.532986492249091, 1.368157626742091, -2.996278911375891, 1.180587738377175, -1.172736098947577,
                 -3.033440537456964, 0.240827440148848, 1.274554856316978, 3.664070614586986, 1.450239057462672,
                 0.43774922918318, -5.139121137322196, -2.184239731700786, -1.722673663384784, 5.003903006525603,
                 1.555677376370723, 4.592628073610951, 0.031529946446351, 1.962038675057533, 1.311519327333439,
                 2.97144042923956, 4.358628571380574, -3.02091040718656, -2.026289674016738, -2.179100342988682,
                 0.199561404454555, -0.642268764969934, -0.48430217469127, 0.867697193885426, -3.428256695897337,
                 4.749688683748538, -3.013096235466324, -0.896473471506158, -5.404408901826174, 3.25190012448197,
                 4.700501169041437, -0.485841922084629, 2.129019854577554, -5.743986607455017, 5.05072704099386,
                 5.290426760843522, 10.017074714718186, 4.109783454482344, -2.171588999232304, -9.143973237995885,
                 2.158096752256321, -4.288300573083361, 8.440796859622568, 0.622933708201643, 0.366857770589574,
                 -5.372666211859802, -6.422211641960874, 12.41021882450194, 3.749357041671196, 2.769912683036556,
                 3.508222243674678, -5.740173772592829, 6.606652001961278, 5.630677773801195, 6.253155297611987,
                 4.559034813600619, 0.191335026648024, -9.72469888506327, 16.676435885179576, -11.815244699143054,
                 -2.963619615334862, 2.644195062564698, -3.504534753584204, 1.539473850852911, 3.24795330924284,
                 4.136178478443613, -1.415042231009744, 0.180777361594801, -2.516794720186608, 6.160828524988144,
                 -6.794575512958723, 0.075817729728995, 0.72736717628741, 1.668305542072184, 1.253458327957929,
                 7.270581180009739, -5.020680731171347, -10.710260791368778, -3.482314899976028, -3.159956937857408,
                 -5.264233710547034, 7.244641579264396, -10.641144576600027, -0.028015125221679, 0.21956976936331,
                 -2.551308504381034, -5.624763316256503, 5.82275011984547, -18.83984795424589, 2.287665332808314,
                 3.202650930764413, -0.143157181428193, -1.939337105017224, -7.297486396599838, 4.714811455331595,
                 -2.285923849103187, 1.571914512358247, 1.18868883861133, 0.665125048665022, -3.62112489105088,
                 7.315055227559002, 2.59208112683563, -3.604968061282964, -1.269251512788574, -4.603628537664914,
                 -6.186864603084382, 8.62584019419564, 15.640620254332642, 9.339712043883617, 0.70056492461347,
                 8.033185101455041, -8.984569662115023, 8.756788125840803, 3.000294638756253, 1.55270194363742,
                 -3.861290375479539, -4.675448372631536, 9.251215593601152, -23.607840924556356, -8.695565550492798,
                 -2.989076129194713, -4.739435400680438, 2.835678457877207, -0.098166653883445, -12.599368055638564,
                 -10.371683803332736, -6.880266107606954, -13.323520170751154, 0.836643098136934, 5.174040794619552,
                 10.49263721553011, 6.802053133291294, -6.116168624020712, 0.612168202208346, -1.561678744412428,
                 -2.961017400369882, 14.555633724082611, -2.408921040101399, 4.164763222501917, -1.288537113083328,
                 0.52492159991554, -0.419743809901388, 6.847306921165118, -2.417811780641292, -7.702891475003848,
                 4.161137142387705, 1.482037717792366, 1.287113176047423, -11.780576083757202, -13.704438843176279,
                 2.252970644465878, -4.628978648000722, -8.44852567895537, 4.501241923053804, 3.218082210067639,
                 -9.096692746700437, -6.359766478822383, 2.458766877052462, 2.747239663468253, 11.352826871439879,
                 1.690071756894771, -4.94464758404481, 1.287478245120863, -5.514465163301222, -0.436320463384376,
                 -2.460885613501708, -2.766872615896895, -5.244256957948294, 3.839764805067356, 1.974486773795782,
                 -3.850467595501656, -5.719184031653332, 3.112555194710462, 2.191998679485594, -0.13026077572474,
                 2.105310692725196, 0.544816987896791, 1.846346039423678, -0.509784805488332, -8.19139781918348,
                 -0.226849791351708, -3.085179788781892, -5.699211118481947, 3.293594390108838, -2.909444711189409,
                 -7.764958328853763, -1.166897030507534, 3.020313808115756, 6.799166924533074, 10.7746541697421,
                 1.525039302686581, -7.554231411053715, -0.587013107919599, -1.300759655302306, -4.080716282648349,
                 -2.186465568681216, -2.406484706468079, -6.958047179121224, -3.235753723041708, -4.304227928343334,
                 0.206370537347841, -2.625027214135385, -5.715311685851154, 1.3755895996309, 7.31396280189546,
                 4.460614374892582, 3.19529761978484, -8.190318873976533, 17.720929604353678, 1.079903507675288,
                 4.032046572169589, 6.846170634331792, -2.990923968785385, -1.01384879195201, 4.210550185586089,
                 0.764711025819911, 6.307647053631859, 6.791811206494187, 1.358857762637734, -1.624613814301878,
                 -10.150256933841442, -8.955086516370987, 2.41129571860055, 2.991835863170325, -2.084105485259767,
                 -0.387882836033353, -12.037911290804784, 0.784781445004331, -3.51258593199461, -6.358051224678802,
                 2.354270933909405, 1.762978551917006, -7.508929712507494, 4.153956520027669, 8.430589071222315,
                 0.112995451436564, 0.371936786139806, -7.025568330781, 9.769013689448464, -0.640793257766108,
                 -11.677067837314976, 1.451572748927877, 1.171502164469018, 1.692647658091279, -0.881205326796837,
                 4.268282553052323, -5.637459471007516, -4.897450001353112, 0.171735893079777, -0.9211330468045,
                 -2.490514871584582, 9.607046944744821, -7.206797204798768, 4.349647727744751, 4.621339140117586,
                 10.832359506208546, 5.100626759149631, -4.076131311364439, -0.932775487935339, 0.50265512140222,
                 -8.6858164471001, 15.294166113376837, -5.994248698329602, -11.694453405846096, -11.801285777570579,
                 3.672120136902007, 2.62662795338849, -6.295270647326517, -1.069859934951232, -3.811330299405138,
                 1.594900688552953, -0.822649421610342, -2.996102354897966, 0.011410712161871, 3.804973955584018,
                 0.29095969601554, 0.154412566421657, 6.009123692794804, 1.048840941857638, 0.1142053347247,
                 1.041139886749876, -1.766488251223224, -1.232261508678568, 3.296071326639848, -2.373608140335303,
                 -1.250158758539599, -0.720873134553766, -0.615677318700518, 1.403259047870607, 3.005529442348132,
                 -1.215626145907597, 0.042094269477194, -2.231879359014662, 1.611360878893429, 6.469735186892669,
                 -5.818552929715837, -0.916929571232854, -0.366611990060092, -5.12995032472354, 6.905064130414796,
                 -6.544579871223458, -7.16220048221164, -1.218392721965018, 0.028386972148047, 13.304659303178061,
                 5.212031101975595, -0.198414575621711, 2.043938045553383, -0.295480321457095, -9.738766031154933,
                 4.373893439532715, 5.133821298601127, -6.818797238217159, 2.032166191824439, 1.299068415608013,
                 -5.282486677908174, 8.79704452443048, 12.849891231025046, 2.787156804154204, 6.225881805493842,
                 -4.429387659772509, -7.4036051874247, 3.478946685666019, 1.632891481413625, -1.105751993528384,
                 3.486878591381191, 2.633938629834198, -3.834544259817514, 6.338661537296378, 0.08440220487435,
                 0.891537452927858, 7.386752241464462, -3.143341265990022, 0.17569569189933, -1.189840684944929,
                 2.063745656499299, -3.873741753209196, 7.631037421790815, 0.870407519991694, 5.416923136109038,
                 -4.796174802688587, -5.441484022053353, 7.113220057014851, -4.021007101784125, 1.093337823554031,
                 -0.723713415910556, -11.778856908851662, -8.947505317275446, 0.371697970006654, -3.368349776966538,
                 4.657443083154591, 0.453955538023004, -1.740701616944006, -1.65220541740956, 1.029317693975664,
                 2.46194490699664, -0.336732006333566, -0.552403825578577, -8.452671777634976, -5.633844858472623,
                 4.461702367049373, -3.779157969724202, 2.769245822243623, -1.006803537946152, 1.372993145527169,
                 -2.883759917737196, 3.935344741349428, 1.588807880940938, 1.666332742486905, -3.345107247615156,
                 9.018878681488209, 4.966343941267382, -2.378406115718362, 4.984115491643569, -6.369451753840427,
                 -4.810457553048149, 4.695631510723671, 8.870216904135045, 1.851266423773728, 0.47853186190453,
                 2.284171420203474, -1.699791507685024, 4.11585412102773, 2.079681507808699, 6.064422880045283])
            self.M = np.reshape(self.M, (30, 30))

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x + 1 - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            y = self.M.dot(x - self.o[:self.D])
            a = exp(-0.1 * sqrt(1 / self.D * np.sum(_y * _y for _y in y)))
            b = exp(1 / self.D * np.sum(cos(0.1 * _y) for _y in y))
            return 0.5 - a - 3 * b + exp(1)

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-140, 140) for _ in range(0, self.D)
        ]


class C09(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-41.03250252873486, -35.70280591875908, -48.66938576680659, 94.51946988004894, 31.68700466174738,
             99.69508270219342, 30.778279925351967, -31.041222172110807, -46.21010370947247, 27.26190010072706,
             -2.093622677920422, 22.246274570582585, -42.887366421312436, 89.88377145577851, -6.731523713182725,
             97.86439204258224, 49.49993772881544, 23.210695390854696, -81.36716857155828, -20.15688556597543,
             36.692155371634726, 44.37408948075327, -15.984549833405907, -49.68391424581281, 98.3715576810595,
             0.127593155843627, 61.709914317965655, -84.0189999580673, -35.39565398431638, -5.143979333218638])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x + 1 - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def h1(X):
            x = np.array(X)
            y = x - self.o[:self.D]
            return np.sum([_y * sin(sqrt(abs(_y))) for _y in y])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-500, 500) for _ in range(0, self.D)
        ]


class C10(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-41.03250252873486, -35.70280591875908, -48.66938576680659, 94.51946988004894, 31.68700466174738,
             99.69508270219342, 30.778279925351967, -31.041222172110807, -46.21010370947247, 27.26190010072706,
             -2.093622677920422, 22.246274570582585, -42.887366421312436, 89.88377145577851, -6.731523713182725,
             97.86439204258224, 49.49993772881544, 23.210695390854696, -81.36716857155828, -20.15688556597543,
             36.692155371634726, 44.37408948075327, -15.984549833405907, -49.68391424581281, 98.3715576810595,
             0.127593155843627, 61.709914317965655, -84.0189999580673, -35.39565398431638, -5.143979333218638])

        if dimensions == 10:
            self.M = np.array([[-0.040348736997622, -0.57547478301341, -0.389765935131311, 0.140885590781398,
                            -0.144670835908614, 0.115450535800564, -0.518304793509814, 0.149470047989466,
                            0.386138410795004, 0.145230906380518], [
                               -0.065866355431932, -0.257122057117828, 0.298076832606074, -0.339838329245853,
                               0.36335902728584, 0.69140513648482, -0.028135015592, 0.214592766458436,
                               -0.21337789362989, 0.150888625576118], [
                               -0.154282201350543, 0.233990300992969, 0.196071677766854, -0.516011967110319,
                               -0.265721705059327, -0.060905234855331, 0.017107816544837, 0.459602625258773,
                               0.548125243387881, -0.174490925899168], [
                               0.363857337521185, 0.622123871321307, 0.051207493885242, 0.281214293456459,
                               0.077610534696611, 0.38475834924149, -0.410410905293451, -0.035659107285799,
                               0.245258564359423, 0.122242429111897], [
                               0.050671935660746, 0.081648277010661, -0.034403618638332, -0.604379533928053,
                               0.029715665982435, -0.255779368776897, -0.321832198773717, -0.454789288464763,
                               -0.04205042244723, 0.49580649289475], [
                               -0.580165394752893, 0.155095038022222, 0.210065625307939, 0.265731671126386,
                               -0.481942328396859, 0.107406464626288, -0.031135086609527, 0.076014134025855,
                               -0.182793563791557, 0.490558019238156], [
                               0.525119522958187, -0.007317154673771, -0.078860784151735, -0.118095618729855,
                               -0.388828033497333, -0.11024307003898, -0.140281776355321, 0.496093855143328,
                               -0.517431971433034, 0.084650093567346], [
                               -0.221934029584865, 0.08937666134435, -0.161579047653623, -0.202383925865339,
                               -0.358394121525824, 0.305489267359872, -0.319803594563591, -0.329350210528224,
                               -0.279367850055518, -0.604257992994783], [
                               -0.14024729892381, -0.085950562359788, 0.562192927749687, 0.15621897836716,
                               0.259175107289986, -0.401255905586421, -0.564318107548709, 0.125407586464797,
                               -0.135691838706447, -0.227388734136487], [
                               -0.392433919094433, 0.338788001793312, -0.569620515414919, -0.064134166835177,
                               0.436408519589215, -0.115926326668514, -0.12994296127687, 0.36903024504463,
                               -0.213067396847211, 0.016735182121047]])

        if dimensions == 30:
            self.M = np.array([
                0.210498733952046, 0.044384555963486, 0.039557467453157, 0.322626150860574, 0.090728331050138,
                -0.046559513284792, 0.279058079389439, 0.216086043131122, 0.001910966340658, 0.005653042988124,
                0.055129408353881, -0.157826900224939, 0.126895165086409, 0.21964276539513, -0.030777527838514,
                -0.174795699173724, -0.071684319926197, -0.435002022420347, 0.300879515782067, 0.196965690878292,
                0.015408513092551, -0.088767209425526, -0.056721005316589, 0.004563253603378, -0.411103445073825,
                -0.098798851715865, -0.017178459384374, 0.223973235768929, -0.038630814336146, 0.153324298640425,
                -0.123853898371964, 0.006208844977804, -0.196968610000223, -0.297886862654356, 0.337304789619692,
                0.151336922415474, -0.190737243250796, 0.371272076371965, 0.05154807935738, -0.089924110414285,
                -0.147609151282806, -0.083802701820601, 0.017052091139248, 0.207360614681, -0.014730657062048,
                0.030357000089238, -0.38493302429957, -0.098471468276102, -0.24989513579197, 0.265831026621616,
                0.039327358400047, 0.102952558500907, 0.112835401766515, 0.075442015984702, -0.050054414064672,
                0.152147800707443, 0.275356821244483, -0.075104525195784, 0.130811841464527, 0.128154556261642,
                -0.247552776888796, -0.170900469984963, -0.025609465501807, -0.074857181493926, 0.06402729466598,
                -0.249589575845155, 0.291141404096232, -0.151955001483896, -0.182101792706067, 0.177528281451576,
                -0.11080027107009, -0.101265961425373, 0.05885945181901, 0.282705041568106, -0.075385821998282,
                -0.183086148754718, -0.03660323576297, 0.095170386364688, 0.004340336079074, 0.000556182984035,
                0.321477491995385, 0.031769482356446, -0.111377507159358, 0.532122152715201, 0.088882029439331,
                -0.098799193147706, 0.101408217806575, -0.110636423867126, -0.211274352044764, -0.155079279400244,
                0.025663646265114, 0.102509721975959, 0.309345681084264, 0.070334750076729, 0.097241604458889,
                -0.273266320148514, -0.239907149554807, 0.150740659614263, 0.069994908090163, -0.039122490587809,
                0.125893036315579, -0.340093749710236, 0.154051275851787, 0.186934934550147, -0.25121208048304,
                -0.196077026994064, 0.01583365232178, 0.55909380072088, 0.112181997526451, -0.008728203934299,
                -0.217367189352171, -0.106529307174376, 0.050536269466879, -0.006551233713265, -0.045903154193401,
                0.167087870245007, 0.002122466601521, -0.012682827041684, -0.002595974426726, 0.093386409335028,
                -0.096757718998012, -0.039650722973667, -0.056221933017481, 0.247299467670382, 0.12500615760951,
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                0.405036010088252, 0.090356343227339, 0.443504939597125, 0.041746547462442, -0.143522086623263])
            self.M = np.reshape(self.M, (30, 30))

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        if dimensions != 10 and dimensions != 30:
            raise ValueError(f"Dimensions must be 10 or 30 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x + 1 - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def h1(X):
            x = np.array(X)
            y = self.M.dot(x - self.o[:self.D])
            return np.sum([_y * sin(sqrt(abs(_y))) for _y in y])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-500, 500) for _ in range(0, self.D)
        ]


class C11(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [0.786412832829728, 0.224970263937584, 0.534200883186777, 0.708371248558908, 0.163080926857473,
             0.768660589584868, 0.1762692231182, 0.310310542254487, 0.279811607250377, 0.825543830090833,
             0.847363744014823, 0.442103825982325, 0.84951329245954, 0.523004716844064, 0.044699072032802,
             0.792400388660219, 0.292824262720788, 0.178722825110973, 0.549380820517875, 0.352736549012222,
             0.080102993555225, 0.853135372349337, 0.790965386853156, 0.951634097517732, 0.809945865440195,
             0.313724260202943, 0.241711589286433, 0.546972335229794, 0.270900015013911, 0.389639306011642])

        if dimensions == 10:
            self.M = np.array([[0.089633918378866, 0.512130446273224, 0.485514077203757, -0.252958672779915,
                                0.252770541915915, 0.282726797116408, 0.011481584098212, 0.501645726490125,
                                0.182717825231853, 0.031348100000026], [
                                   -0.280115239342013, -0.237293103798464, 0.161291392367593, -0.283707496604443,
                                   0.364811930990744, -0.062761689119337, -0.739185429285342, -0.191267363255477,
                                   0.163852853571301, -0.108948351728692], [
                                   -0.054128359479101, 0.280409298113112, -0.705443683949621, -0.251735861726835,
                                   0.296849031180373, -0.090813013702513, -0.065957070182011, 0.30050375022829,
                                   -0.272322162400478, -0.303711147333872], [
                                   -0.452847886193739, 0.236523261801994, -0.24521824465, -0.081379588794718,
                                   -0.24525780464972, 0.37821495392415, -0.18193070994583, -0.056246704353941,
                                   -0.093664221220625, 0.651150911995366], [
                                   0.361083640107602, 0.399352960578869, -0.140221518169126, 0.501290666120365,
                                   0.227775075396437, -0.344871729183228, -0.325346508438507, -0.093040278786845,
                                   0.207431987018326, 0.332909212543853], [
                                   -0.327366108426422, -0.034166615485878, 0.168579539272942, -0.231702366911964,
                                   0.292800433946541, -0.648132837341469, 0.361448041200678, 0.046154155969474,
                                   -0.131239528768862, 0.392112292683837], [
                                   -0.416908196358713, -0.161816318805297, -0.179541440127106, 0.415157611173291,
                                   0.440167850594149, 0.25787201771071, 0.30374321406454, 0.077672586210717,
                                   0.481631833482542, -0.070029679148627], [
                                   -0.082769326821527, -0.296458567384547, -0.095392668680313, 0.079070267285698,
                                   -0.410370494920092, -0.277529903950391, -0.232372087468556, 0.707837046116306,
                                   0.291065711969018, 0.068801000678567], [
                                   -0.314851017910477, 0.037408462116577, 0.292343317100224, 0.548829235267724,
                                   0.065696886904918, 0.012681873453724, -0.181476896332993, 0.21693059635943,
                                   -0.634666974725221, -0.159748150960876], [
                                   0.433769285221923, -0.520307620610873, -0.07481810354657, -0.026992353432197,
                                   0.388158769389856, 0.286598385013371, -0.018393693541829, 0.231063340808058,
                                   -0.277653756652504, 0.413729117675976]])

        if dimensions == 30:
            self.M = np.array(
                [0.213664507653598, 0.081181994864055, 0.109287504638465, -0.025177866704494, 0.022222285306807,
                 -0.042656953236281, -0.064708019281558, -0.166881530892857, -0.049029069752717, -0.352822537935909,
                 -0.10560659182506, 0.172325114774526, 0.151840000333581, 0.226141562503423, 0.098785038088718,
                 -0.038046518396688, 0.064354476751985, -0.397839588660246, -0.035803778238277, 0.143522858437253,
                 -0.132896510459094, 0.0776878752635, -0.230960330142614, -0.065023068412763, -0.324799719792717,
                 -0.394410999051323, -0.064406717392709, 0.113744829639383, 0.311387845557846, -0.083797609752818,
                 -0.271700088866788, -0.019398284664685, -0.147690031630542, -0.14646895298845, 0.009734059809619,
                 -0.360263733874311, -0.08446685711877, 0.074923689193188, -0.163935979878233, 0.235507396752789,
                 0.109294373655602, 0.275570585394534, -0.342781812122196, -0.097283082956871, 0.000965683202336,
                 -0.364649787402838, -0.120741535426983, 0.081333703172679, 0.187707474611139, 0.012291646647864,
                 -0.24973965974932, 0.147605133728092, -0.236914164871624, 0.140203023828278, -0.090596746744275,
                 -0.079951967559402, 0.041339061451822, -0.143083898074293, 0.215812964283571, -0.099342527132328,
                 -0.067990719526256, 0.265087739087109, 0.069641500406529, -0.075142020476545, 0.342414425640308,
                 0.193955025198242, -0.119407721016019, 0.182485692744089, 0.078232269648152, 0.201926600459772,
                 -0.118697625348229, -0.043262798962201, 0.372144060227865, -0.015090449753815, 0.191694088533503,
                 -0.158605317529149, 0.112058863065475, 0.02590233184065, 0.203619627876814, -0.064290403099129,
                 -0.02432596986466, 0.049145901961444, 0.229396848321773, -0.05297004437931, -0.065000670782875,
                 0.070701153673056, 0.453255361840545, -0.083935114638238, 0.31146341510963, -0.112829838156669,
                 -0.030968898341742, -0.050888797506771, 0.030830254756601, 0.04178819557194, 0.065658071283464,
                 0.450923351464843, 0.214706791003811, -0.323207360495052, -0.008354103400074, -0.053133505933914,
                 -0.195866183390291, 0.187102471622905, -0.206804305716109, 0.101819425389, -0.11082652699642,
                 -0.39711167566239, 0.123494232660844, 0.053736727436837, 0.096036627871671, -0.019258600848078,
                 -0.219821668806651, -0.253908923606591, 0.005089036974723, 0.127991628463459, 0.037075803520335,
                 0.010378302018138, 0.143684554656654, 0.095817813086981, -0.050150939862002, 0.381333412629458,
                 0.226288151720664, 0.24606116722381, 0.106911720534849, 0.47292916025144, 0.080821596642439,
                 -0.106639568044331, -0.25871955956032, -0.067303307998019, -0.223306723802896, 0.118314382149731,
                 0.092493331574002, -0.160569418944115, -0.239227946040776, 0.159833124584156, -0.097589784005531,
                 0.088561998405886, -0.176607551065602, 0.220391576923025, 0.118673673892537, 0.172529373109131,
                 -0.052521621702833, -0.302376571169939, 0.057527555491466, -0.051198984930294, -0.151934969640591,
                 -0.20288907211878, 0.237694257469852, 0.011731349236642, -0.121683054008454, -0.077562196214513,
                 -0.023341124631582, -0.106367398061812, 0.139185062166589, -0.023708842343116, -0.122924349219742,
                 -0.317270751931269, 0.09085932216237, -0.004003755978159, 0.081504361166746, 0.051004055378783,
                 0.087122807098326, 0.065197475901265, 0.326987038415912, 0.149191719081344, 0.059533213153922,
                 -0.289471752269275, 0.036292592579316, 0.051991150593147, 0.423393392470416, 0.14243832552759,
                 0.26694616322671, -0.033149461153889, -0.06238762331634, -0.017742808969348, 0.224077199443182,
                 -0.164572449247675, 0.081131000428636, 0.411086190636973, -0.270016145919628, 0.025440356389857,
                 0.217340969946372, -0.010887941365408, -0.105414165411544, 0.028640113752392, 0.245192353428576,
                 0.123453698955525, -0.22027448510549, 0.080008470401683, -0.200064078714725, 0.409080331693645,
                 -0.01164832823722, 0.149285163400367, 0.212696285382057, -0.036842592163519, -0.184563269192621,
                 0.099675386172013, -0.104962991127113, -0.351644037772175, 0.003612814494479, 0.119290352639962,
                 0.014038386652626, -0.078680586178529, -0.257539407999236, 0.356517750088968, 0.065999684745291,
                 0.244698740520543, -0.16125727875757, 0.200412631256186, -0.047032406403128, 0.093230368282809,
                 -0.308047033323461, 0.302782029048974, 0.0322661459595, 0.106587658429326, -0.168271630981503,
                 -0.070035690780841, -0.051571249957414, 0.097566654836796, 0.098375409745765, 0.151603976234746,
                 0.057525783219635, -0.286426352771457, 0.024343824914064, -0.169780687940365, 0.022380508340654,
                 -0.030982311876094, 0.415717134652659, -0.197521326266871, 0.088420077973385, 0.011114954697741,
                 -0.263872664453069, -0.375124011890858, -0.011430439277577, -0.07452629391033, 0.016003087509299,
                 -0.065727200479196, -0.373774336560803, 0.105840318864163, 0.100418169473233, 0.073219551853995,
                 0.200163577534198, -0.039678659465808, 0.379527614131999, -0.099745969482936, -0.221373690414668,
                 -0.148184040663515, 0.218923185321619, 0.16342688952909, 0.106160271957917, 0.128647167415555,
                 -0.263722004680904, -0.073799506832126, -0.130373260973961, 0.05112747364289, -0.275651806010854,
                 0.164362590475763, -0.193891035029316, -0.231705892488554, 0.203407120866966, -0.180088567449336,
                 -0.217043829626228, -0.065298093403581, -0.168646979873782, -0.249526662066186, 0.142398466204126,
                 0.184373897375203, 0.214617996769777, -0.037763990107245, 0.153370753506811, 0.029960427580481,
                 0.027381106799167, 0.025298590700846, 0.085102913098931, 0.235161900850573, 0.088822124808788,
                 -0.147746689766744, 0.287007437228783, 0.059680973101153, -0.083383065195545, -0.031229323155654,
                 0.031553099382079, 0.40509260167075, -0.066928120228784, -0.029895161415616, 0.160569988999854,
                 0.16678557709598, 0.254241427727434, 0.231808030036861, -0.219633335492758, -0.018225943045591,
                 0.205429075567088, -0.287803693680246, -0.159043658666998, 0.00879409803725, 0.165691311194281,
                 0.193758433635177, 0.054989913382989, 0.187140153188654, 0.381913634751314, -0.148889172210388,
                 -0.193804968887996, -0.065126964033178, 0.301214940522026, 0.235589681539659, -0.103550074605781,
                 0.108383273427244, -0.105180685554545, 0.023625536573582, 0.01996270869354, -0.197178099155744,
                 -0.005579002923688, -0.105996290889411, -0.201484156798754, -0.016580477043595, 0.096437307973893,
                 -0.265258984515337, -0.038285680241242, -0.297437900623478, 0.110282050166409, 0.340831537665284,
                 0.353344803948875, 0.023969199411952, -0.037508048676323, 0.084467945077494, -0.101339199091876,
                 0.43127923493617, -0.012803021754092, -0.187066022130407, 0.045422882183782, -0.145164655232891,
                 -0.188010538259573, 0.072561095841883, -0.215861415006047, 0.340625060094133, -0.271380094003135,
                 0.011550630486271, -0.021562277822991, -0.047039032262669, 0.24569627934439, 0.06878748082518,
                 -0.013753922818236, 0.128800076934912, 0.251945144841963, -0.065565948950666, -0.247270548381727,
                 -0.049324630517431, -0.283284532614162, -0.056856284381622, -0.038971423817489, -0.252726173186928,
                 0.351734439125858, -0.110658990096839, -0.106510561670422, 0.03737869055179, -0.115271643539905,
                 -0.196770239210232, 0.068745507207565, -0.202290191092434, 0.230742566811053, 0.242089942893867,
                 0.009670899760703, 0.19086441968746, -0.162303316721271, 0.064683931277254, 0.164443149200815,
                 -0.323437173681877, 0.298292486419165, -0.287839297934076, 0.103447987260199, 0.066558230735603,
                 -0.00811153653084, -0.267028782102504, -0.109803324276942, 0.030622270397094, -0.238104734999257,
                 -0.210337042122685, -0.039664653707487, -0.272682220581616, -0.289034258391312, -0.056713148536939,
                 0.041560290546935, 0.135947625574874, 0.288994345741835, 0.171226457682862, 0.020154597183878,
                 0.087557724549144, 0.128411957307537, 0.266870366178292, 0.074920714589854, -0.178633022967889,
                 0.058380141741122, -0.067241741998389, -0.107729007864175, -0.056331784327996, -0.168481007769024,
                 0.262125246984163, -0.007371786978638, 0.164927818594684, -0.036933870960258, 0.267509327915296,
                 0.368795934127986, -0.04754732992007, -0.164871757638282, 0.316046246052052, 0.326208946153877,
                 -0.210383643418226, -0.148743568976932, -0.27228848616751, -0.298371974359424, -0.079114502261498,
                 0.05112487825332, -0.103950446102005, -0.01564162533912, -0.270929678785201, 0.227289734744482,
                 -0.122956418941428, 0.102700134174179, 0.066767014869907, 0.049550920130668, -0.033845164172596,
                 -0.086630774192244, -0.114904967536104, 0.070689120361513, -0.034071515269466, 0.122164936018725,
                 -0.185544714908452, -0.269907389645905, 0.053128966344123, 0.063580062903748, -0.230905233848124,
                 0.130786631191676, 0.203748730532203, 0.130738123136379, -0.063121304824537, -0.291378414288447,
                 -0.043225535031358, -0.01069469449417, -0.116151001188632, -0.23721367266419, 0.350947535209623,
                 -0.186552245007249, -0.152639157418694, 0.254048004058081, -0.092165966765819, 0.455200922690338,
                 -0.07128324467846, 0.136761821923458, -0.205729179281477, 0.069566872999856, 0.148165641059529,
                 0.094081789475437, 0.164525371012304, 0.530381662137654, -0.125311053655768, 0.103697488442664,
                 -0.121905535749908, 0.056068298081543, 0.006703334135084, 0.036583551377082, 0.19140471505715,
                 0.039561231445405, -0.055422919551433, 0.022569682208961, 0.296355942714072, 0.072278015477743,
                 -0.112500040523406, -0.080236580672682, 0.19033073835967, -0.174276465756524, -0.065077925843275,
                 0.115236637819618, 0.066402495691896, 0.114332383707042, 0.305300264476914, 0.041924689523625,
                 -0.114010854564669, -0.352544042259548, -0.244055210901362, 0.13019634886009, 0.269526775862334,
                 -0.00489419108819, -0.3059705776172, 0.311548390724184, -0.141572289005789, -0.203176699858192,
                 -0.026095435739131, -0.065157179843688, -0.038350549407962, -0.073662373827209, 0.142157446085444,
                 0.157373461407893, -0.046099550200634, 0.224210978651247, -0.298335544413649, -0.073612901848245,
                 -0.159247698378578, -0.051909721672664, 0.145993068464936, -0.36519319549698, 0.032297295074329,
                 -0.109562705494831, -0.167000876200342, 0.043268603623865, -0.035859010210215, -0.484116022339656,
                 0.09192729037904, 0.155163485816933, 0.224679527158001, 0.003313807587358, 0.011812567832784,
                 -0.079164184231885, -0.079673141928438, 0.047991031562159, -0.093766613853708, 0.31417752040776,
                 -0.056911358204846, 0.362675633792648, -0.060338425113277, -0.280182381876974, 0.018772162685462,
                 0.051300062700589, -0.049623674415283, 0.176261753739058, -0.060467833054284, -0.076986055167777,
                 -0.086777151803824, 0.049098470056174, -0.187178075938113, -0.014362935091577, -0.12289914106784,
                 0.118233415764786, -0.341422902426496, -0.167949488567614, -0.105989559529699, -0.060591056127266,
                 -0.15239285521963, 0.002181073424684, -0.462669300934778, -0.297768213927992, -0.23380321131751,
                 -0.378516156455358, -0.1433149891874, 0.047248285574843, 0.072629998044244, 0.149384001121451,
                 0.237240860059944, 0.140303661447484, 0.20066037622952, 0.035463732796464, -0.106195572441474,
                 -0.031516316526196, -0.002151701441264, 0.1120768051884, 0.270901510772577, -0.200351435603717,
                 0.03852665027653, -0.418916524751717, 0.143638180001652, 0.061776304496446, 0.033980452524636,
                 -0.193528759911095, -0.110620722807679, 0.109607952267434, 0.03346002081992, -0.014672688437202,
                 -0.043724266514642, -0.280786021801147, 0.235169251004673, 0.118943568855134, -0.366995339144498,
                 -0.090339156130256, 0.258102936725341, -0.082642825321426, -0.177874017144745, 0.001268583195314,
                 -0.105702469885669, -0.090199054235473, 0.094582271703797, -0.043622730291984, -0.345727234198456,
                 -0.209942734609341, -0.30615350519884, 0.090529904558773, -0.041653675014824, 0.255573298859712,
                 -0.100282747841932, -0.309584581927906, 0.124877004928111, -0.223740676264403, -0.031268448227269,
                 -0.066373754125275, -0.222674185629732, -0.403563586099264, 0.212127520816293, 0.167001816786667,
                 0.094847203771669, 0.173262511782359, 0.085443515162292, -0.069914437636284, 0.102643402622927,
                 0.055440475109441, 0.085951891879032, 0.172992547024373, 0.262905477453187, 0.224743205594987,
                 0.192540830845651, 0.110061418307678, 0.113827332043669, -0.168469541416164, -0.04800537454653,
                 0.103346160688271, -0.180941765207566, 0.011325240855003, -0.37937643896595, -0.182592630006882,
                 -0.221956531577796, -0.027987786008159, 0.116676051648963, -0.077346609370271, 0.016773991056786,
                 0.030200744490138, 0.418769276358264, -0.283443734028498, -0.294586681085427, 0.197167046347308,
                 -0.163506777011674, -0.103837075932755, 0.134058921059171, 0.081801487643389, 0.109353676269996,
                 0.327360471359169, -0.28072048808365, -0.163555479309503, 0.31919381862241, 0.063611972539563,
                 -0.214486982533365, -0.157654777857054, 0.180549631755488, -0.055285854531161, -0.186429790904586,
                 -0.068249197353824, -0.064677706786589, 0.152817049718154, 0.144643068264453, 0.056012720322906,
                 -0.382743611197267, 0.074505674218174, 0.050078384096382, 0.030018371634662, -0.384023043648669,
                 -0.184612676085607, -0.055459493703995, 0.10084350694568, -0.081489460313665, -0.021712726717977,
                 0.262680866816756, -0.221178011925896, -0.086134078535059, 0.031300536165372, 0.011861695055184,
                 0.012964948275638, 0.187786072616282, -0.056369451537001, -0.289348154058947, 0.267823758425312,
                 -0.088036659356012, -0.133230824780343, -0.304146488281969, 0.015965034670596, -0.050127979326313,
                 0.106949329082003, 0.103272761307408, -0.07919152944579, -0.073430347934756, 0.026235112233403,
                 -0.021541007721428, -0.293978552332072, 0.023684646076276, 0.156645243190832, -0.048501664763,
                 0.197787704307392, -0.194211661422967, 0.080180820135367, -0.496906269530592, -0.104584005315891,
                 0.258873613013138, -0.259649539738847, 0.123811379191117, 0.111293522081587, 0.181939407958772,
                 0.294835878808858, -0.029209022921241, -0.217723946054288, -0.097404777024707, -0.052429134815045,
                 0.040356030871308, 0.393236833225354, 0.175557179390611, -0.03094063400247, -0.177963860281272,
                 0.449916685915366, -0.213456939438994, 0.058508695099448, -0.094405596522676, -0.008625355411711,
                 0.039160284839969, -0.098625835561265, 0.03408199804298, 0.28011442570154, 0.268665001951654,
                 -0.05434462021136, -0.110027495654122, 0.08235063573064, 0.205944827529079, -0.102051034805319,
                 0.041742870426194, 0.0207325192601, -0.092449553345455, 0.289687544712165, 0.200232137666344,
                 0.324562283237417, 0.184648348712247, 0.022446625011167, -0.217977077322926, -0.245608197072427,
                 0.223028741194813, -0.191885672937206, -0.234565861266814, 0.163142136227469, -0.034483774167611,
                 0.136829944775942, 0.010659321371311, 0.043886061930328, -0.10015800029478, -0.260708326142898,
                 -0.209912729155911, 0.074289636558659, 0.192908976804704, 0.05279894151016, -0.083703330879473,
                 0.044462302715181, -0.10922321061719, -0.119520743996587, 0.115312784885046, 0.219184418022002,
                 0.006476796614963, -0.099039640423129, -0.09552952028645, 0.090279102663188, -0.520408674965181,
                 0.189874262576368, -0.095312769672106, 0.023229777012101, 0.114052756900062, -0.095698687766527,
                 0.00435622515588, 0.104383153814706, -0.101851026410131, -0.036031847418115, 0.130809079398388,
                 -0.348176020977506, 0.083868330339566, -0.008027489258884, -0.475250515495636, 0.405075360910557,
                 -0.058330958272204, -0.324799136178965, -0.098826394313126, 0.054201621445373, 0.105539013031662,
                 -0.050165348263293, -0.110988043058241, 0.307823877172309, 0.065638607280896, 0.175611728595002,
                 -0.225527874730007, -0.167576013387015, -0.025898462181195, 0.047977681482798, -0.121614920666241,
                 0.024817395005562, -0.17487826875149, -0.075479150475883, 0.189943924393605, 0.156145281833982,
                 -0.055799107787251, 0.097805210291524, -0.333519405775061, 0.502960649221461, 0.250717861251373,
                 0.037973315883562, -0.117614481392449, 0.150749952794974, 0.100713451865893, 0.194650730577328,
                 0.041542080273073, -0.043075295232123, 0.14346659720724, -0.066416684640206, 0.30945572209412,
                 -0.26079323424583, 0.060654388815103, -0.330908639343534, -0.133139659765055, 0.007715506835614,
                 0.129099603050523, -0.026526508575731, -0.186033367233492, -0.003227201944237, -0.039533548327422,
                 0.095784409010262, 0.493960649899082, 0.054965293016426, 0.118386898900744, -0.089743887601218,
                 0.011108183624052, 0.207327445064612, 0.242443769099025, 0.206198128469745, -0.07993765597308,
                 0.158376106471485, 0.44069018725539, 0.080476930007356, -0.063999018485218, 0.045791410027783,
                 -0.109218662101228, -0.133146601849485, -0.063457328740753, -0.098077643442681, 0.030229627867435,
                 -0.22587886702408, 0.088840219102173, 0.060713279234327, -0.042942042030938, -0.162299014039001,
                 0.166693220455956, -0.036996741605182, -0.03566092366649, -0.415251090818235, -0.033476312360835,
                 -0.134737022651914, 0.178824441680756, -0.065367644831629, 0.070421716758178, -0.380997368249991,
                 -0.001424117236101, 0.111786488541009, -0.315238045340073, -0.564567583141614, 0.075644045120957,
                 -0.04106733803819, 0.001359106777444, 0.342613474532435, 0.18843734841569, 0.016632274309598,
                 0.028151100670402, -0.040804196229357, 0.078149572314004, -0.009015740224079, 0.124083718002042,
                 -0.154078742598099, 0.184197151976838, 0.073620529124081, -0.129123672253497, 0.132520067351779,
                 0.24339516107792, -0.028784979253861, -0.122593877628548, 0.087287100630432, 0.014242700063557,
                 -0.151931107719764, -0.0663705228758, 0.273691836188752, 0.166063639310765, 0.083603614767844,
                 0.012365248008479, -0.096837151548972, -0.323046786194128, -0.002762106981556, -0.162979039714608,
                 0.457731335540932, 0.030662875665406, 0.044328722462083, -0.132362192693964, 0.204392438255486,
                 0.213174368356641, -0.108362051125394, -0.179307935282083, 0.178742296246812, -0.435746945897502,
                 -0.211500176759846, 0.068172045884552, -0.005852335257529, 0.225384707452164, 0.144166264871071,
                 0.041229273569432, 0.118238109507269, 0.053070226894351, -0.013323395106702, -0.005285812892181])
            self.M = np.reshape(self.M, (30, 30))

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        if dimensions != 10 and dimensions != 30:
            raise ValueError(f"Dimensions must be 10 or 30 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = self.M.dot(x - self.o[:self.D])
        return (1 / self.D) * np.sum([-1.0 * _z * cos(2 * sqrt(abs(_z))) for _z in z])

    def constraints(self):
        def h1(X):
            x = np.array(X)
            y = x + 1 - self.o[:self.D]
            return np.sum([(100 * (y[i] ** 2 - y[i + 1])) ** 2 + (y[i] - 1) ** 2 for i in range(0, len(y) - 1)])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-100, 100) for _ in range(0, self.D)
        ]


class C12(ConstrainedFunction):
    def __init__(self, dimensions):
        self.o = np.array(
            [18.889635068428205, -59.265426383246485, 33.25998466165768, 20.152694275194037, -10.734106238462502,
             -90.85053128520764, -12.073899411249897, 59.72307696259165, -37.44193247323578, 25.963111555782035,
             6.251460324561279, 41.478172862575434, 86.54258849813075, 34.94822787072172, 26.864471649916382,
             79.55580868986908, -44.66218241775459, -7.305741544994362, 87.75843366209835, 33.836473236958284,
             84.53385936725138, 80.89850629751817, 48.46967726645195, -82.0758049330533, -98.54273249151939,
             19.55069746505636, 8.33657824668768, 88.54888769408086, -79.08282398956031, 63.254014133387614])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return np.sum([_z * sin(sqrt(abs(_z))) for _z in z])

    def constraints(self):
        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([(z[i] ** 2 - z[i + 1]) ** 2 for i in range(0, len(z) - 1)])

        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([_z - 100 * cos(0.1 * _z) for _z in z])

        return {
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-1000, 1000) for _ in range(0, self.D)
        ]


class C13(ConstrainedFunction):
    def __init__(self, dimensions):
        self.o = np.array([69.69311714880897, 1.509803311435702, 67.6746198312362, 80.43173609273597, 80.47622449424348,
                           51.21092936019716, 52.7723719926014, 17.248465789326257, 52.40150903116374,
                           39.64846247456716, 89.86375903333635, 32.079301315169474, 43.192499277837946,
                           70.79294586561508, 1.48440984483988, 19.8566700417119, 29.502667246412756,
                           34.256788127976684, 12.643016541338264, 78.57234385195876, 26.51647349482587,
                           97.06430708087798, 10.180504722002471, 82.90799886855778, 63.540231382573154,
                           74.78243308676124, 87.20817289266436, 50.779655804893764, 43.05412185616204,
                           33.862234518700916])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        return (1 / self.D) * np.sum([-_z * sin(sqrt(abs(_z))) for _z in z])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return -50 + (1 / (100 * self.D)) * np.sum([_z * _z for _z in z])

        def g2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return (50 / self.D) * np.sum([sin((1 / 50) * pi * _z) for _z in z])

        def g3(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return 75 - 50 * (np.sum([_z ** 2 / 4000 for _z in z]) - np.prod(
                [cos(_z / sqrt(i + 1)) for i, _z in enumerate(z)]) + 1)

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g3":
                {
                    "name": "g3",
                    "func": g3,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-500, 500) for _ in range(0, self.D)
        ]


class C14(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-31.718907007204272, -39.536680684207184, -46.033718058035944, -42.2004014684422, -28.331307546159135,
             -38.64403177375364, -11.313371899853626, -11.717383190039943, -43.345049558717875, -31.46016185891229,
             -35.57742732758397, -45.49638850141341, -4.177473725277878, -26.974808661067083, -46.30991533784743,
             -45.997883193212814, -29.479673271045964, -4.336542960830036, -43.66244285780764, -22.43896852522004,
             -25.89273808052249, -24.221450510218993, -30.3952886350567, -31.170730638052895, -9.859463575974534,
             -16.727846507426452, -44.35226340706524, -33.10843069426064, -7.175153678947718, -4.601421202670486])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x + 1 - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x + 1 - self.o[:self.D]
            y = x - self.o[:self.D]
            return np.sum([-_y * cos(sqrt(abs(_y))) for _y in y]) - self.D

        def g2(X):
            x = np.array(X)
            z = x + 1 - self.o[:self.D]
            y = x - self.o[:self.D]
            return np.sum([_y * cos(sqrt(abs(_y))) for _y in y]) - self.D

        def g3(X):
            x = np.array(X)
            z = x + 1 - self.o[:self.D]
            y = x - self.o[:self.D]
            return np.sum([_y * cos(sqrt(abs(_y))) for _y in y]) - 10 * self.D

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g3":
                {
                    "name": "g3",
                    "func": g3,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-1000, 1000) for _ in range(0, self.D)
        ]


class C15(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-31.718907007204272, -39.536680684207184, -46.033718058035944, -42.2004014684422, -28.331307546159135,
             -38.64403177375364, -11.313371899853626, -11.717383190039943, -43.345049558717875, -31.46016185891229,
             -35.57742732758397, -45.49638850141341, -4.177473725277878, -26.974808661067083, -46.30991533784743,
             -45.997883193212814, -29.479673271045964, -4.336542960830036, -43.66244285780764, -22.43896852522004,
             -25.89273808052249, -24.221450510218993, -30.3952886350567, -31.170730638052895, -9.859463575974534,
             -16.727846507426452, -44.35226340706524, -33.10843069426064, -7.175153678947718, -4.601421202670486])

        if dimensions == 10:
            self.M = np.array([[-18.785768809450733, 33.616954860437176, 26.882113915382682, -10.43306419742936,
                            0.944892894082476, -3.353896433259691, 3.535212733907637, 7.394276941396782,
                            7.790908752641235, 2.091292109983567], [
                               -0.38080580880289, 10.420967284673154, 9.347291913115678, -20.926490513724943,
                               11.4259031588907, 1.10563725749954, 36.87928250597037, -1.910339780463472,
                               7.56116849320932, -9.743066435789972], [
                               -12.341688082549489, 6.362199355273804, 8.248429939792464, 8.089256366417835,
                               0.069234506619011, 2.578624135957486, -0.497340218618186, -2.062722095484327,
                               1.430205147745766, 15.52200376094467], [
                               -17.00654251044467, -12.679306064055677, 51.65812051951116, -3.97661207806369,
                               3.934938475013658, -30.777202564613845, 6.146597147627116, -11.404959107806402,
                               12.694206030880832, -9.395143228117439], [
                               -5.484749154282662, -13.643476981518553, -20.8125786035429, 12.48063177681885,
                               0.844978205995699, 24.830393330514045, 33.83850518570256, -17.003569707093064,
                               -5.293964367404844, 26.065703095424336], [
                               11.422878520470586, 10.221461943150496, -5.999478987400263, -8.935891602574142,
                               3.340791625151446, 3.924548854255449, -6.760571785727851, 14.016300477765046,
                               2.353396935795215, -15.957358828479556], [
                               14.1069797350053, -0.689797572922294, 25.92835826668488, -30.138271725378775,
                               12.953067028884863, -17.125782201118525, 19.122903237509483, 3.85021017121606,
                               14.44987126033593, -37.768641488073015], [
                               1.817162027385162, -4.52289774299815, 2.596064824368431, -3.077970366333548,
                               3.666238380627702, -3.142271967105208, -1.93910379576585, -1.132846020949443,
                               -1.459397119228072, -4.385065305078107], [
                               17.05963501513677, -40.88734304067851, -9.04136854737176, 9.207816613351653,
                               2.483559081696921, -3.135238286666343, -0.515970843448524, -10.448164970351954,
                               -3.97908386413912, -5.010151763870892], [
                               -2.100410473384162, 4.285743492212902, 18.13880373052391, -5.569156622351854,
                               0.020414928764168, -5.531568307180828, 1.750746232557792, 2.018382353850689,
                8.96737078655512, -3.593654241962948]])

        if dimensions == 30:
            self.M = np.array(
                [2.511190264603399, 8.257741094092125, -4.576972024334265, 11.663051871013478, 1.839271199160904,
                 -3.930477981638431, 6.381869129516557, 5.554163663846647, 3.792259995720394, -8.119579509114208,
                 -6.749873116676933, -4.16744839268803, -5.247229107097693, 4.380091021522936, 1.636265206648646,
                 1.545744455341335, 6.729651169519672, -10.841704025647012, 5.27133366266056, -7.315142457952093,
                 5.586455189183544, 0.134360209805352, -4.757951297029205, -7.494064226166734, -5.71422876353246,
                 -3.494974588660678, 16.86892010541546, -6.058600435639238, -11.353403083342686, -1.092357620889664,
                 -1.176252081542598, 0.194672319059377, 9.262321627779777, -4.877620455960017, -3.013825305151295,
                 4.138941347418219, -4.381399078195643, -1.095599959738453, 3.362553950833766, -2.415178728944297,
                 -6.861613142354518, -0.005940467288075, 2.135933445957106, -0.307275749657439, 9.502954614655595,
                 3.789220991977143, -2.76964275770075, 2.672716708411572, -3.374116812054481, -2.041106775198442,
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                 -5.818552929715837, -0.916929571232854, -0.366611990060092, -5.12995032472354, 6.905064130414796,
                 -6.544579871223458, -7.16220048221164, -1.218392721965018, 0.028386972148047, 13.304659303178061,
                 5.212031101975595, -0.198414575621711, 2.043938045553383, -0.295480321457095, -9.738766031154933,
                 4.373893439532715, 5.133821298601127, -6.818797238217159, 2.032166191824439, 1.299068415608013,
                 -5.282486677908174, 8.79704452443048, 12.849891231025046, 2.787156804154204, 6.225881805493842,
                 -4.429387659772509, -7.4036051874247, 3.478946685666019, 1.632891481413625, -1.105751993528384,
                 3.486878591381191, 2.633938629834198, -3.834544259817514, 6.338661537296378, 0.08440220487435,
                 0.891537452927858, 7.386752241464462, -3.143341265990022, 0.17569569189933, -1.189840684944929,
                 2.063745656499299, -3.873741753209196, 7.631037421790815, 0.870407519991694, 5.416923136109038,
                 -4.796174802688587, -5.441484022053353, 7.113220057014851, -4.021007101784125, 1.093337823554031,
                 -0.723713415910556, -11.778856908851662, -8.947505317275446, 0.371697970006654, -3.368349776966538,
                 4.657443083154591, 0.453955538023004, -1.740701616944006, -1.65220541740956, 1.029317693975664,
                 2.46194490699664, -0.336732006333566, -0.552403825578577, -8.452671777634976, -5.633844858472623,
                 4.461702367049373, -3.779157969724202, 2.769245822243623, -1.006803537946152, 1.372993145527169,
                 -2.883759917737196, 3.935344741349428, 1.588807880940938, 1.666332742486905, -3.345107247615156,
                 9.018878681488209, 4.966343941267382, -2.378406115718362, 4.984115491643569, -6.369451753840427,
                 -4.810457553048149, 4.695631510723671, 8.870216904135045, 1.851266423773728, 0.47853186190453,
                 2.284171420203474, -1.699791507685024, 4.11585412102773, 2.079681507808699, 6.064422880045283])
            self.M = np.reshape(self.M, (30, 30))

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        if dimensions != 10 and dimensions != 30:
            raise ValueError(f"Dimensions must be 10 or 30 for function {self.__class__.__name__}")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x + 1 - self.o[:self.D]
        return np.sum([(100 * (z[i] ** 2 - z[i + 1])) ** 2 + (z[i] - 1) ** 2 for i in range(0, len(z) - 1)])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x + 1 - self.o[:self.D]
            y = self.M.dot(x - self.o[:self.D])
            return np.sum([-_y * cos(sqrt(abs(_y))) for _y in y]) - self.D

        def g2(X):
            x = np.array(X)
            z = x + 1 - self.o[:self.D]
            y = self.M.dot(x - self.o[:self.D])
            return np.sum([_y * cos(sqrt(abs(_y))) for _y in y]) - self.D

        def g3(X):
            x = np.array(X)
            z = x + 1 - self.o[:self.D]
            y = self.M.dot(x - self.o[:self.D])
            return np.sum([_y * cos(sqrt(abs(_y))) for _y in y]) - 10 * self.D

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g3":
                {
                    "name": "g3",
                    "func": g3,
                    "op": operator.le,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-1000, 1000) for _ in range(0, self.D)
        ]


class C16(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [0.365972807627352, 0.429881383400138, -0.420917679577772, 0.984265986788929, 0.324792771198785,
             0.463737106835568, 0.989554882052943, 0.307453878359996, 0.625094764380575, -0.358589007202526,
             0.24624504504104, -0.96149609569083, -0.184146201911073, -0.030609388103067, 0.13366054512765,
             0.450280168292005, -0.662063233352676, 0.720384516339946, 0.518473305175091, -0.969074121149791,
             -0.221655317677079, 0.327361832246864, -0.695097713581401, -0.671724285177815, -0.534907819936839,
             -0.003991036739113, 0.486452090756303, -0.689962754053575, -0.138437260109118, -0.626943354458217])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        a = np.sum([(_z ** 2) / 4000 for _z in z])
        # i+1 because we start from 1
        b = np.prod([cos(_z / sqrt(i + 1)) for i, _z in enumerate(z)])
        return a - b + 1

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([_z * _z - 100 * cos(pi * _z) + 10 for _z in z])

        def g2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.prod(z[:self.D])

        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([_z * sin(sqrt(abs(_z))) for _z in z])

        def h2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([_z * -1. * sin(sqrt(abs(_z))) for _z in z])

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                },
            "h2":
                {
                    "name": "h2",
                    "func": h2,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-10, 10) for _ in range(0, self.D)
        ]


class C17(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-0.628245703945122, 0.331024455127249, 0.402617203423807, 0.462742527496583, -0.513329779137884,
             0.288191632492259, 0.41479349370103, 0.916196063289011, -0.427742767473712, 0.811971694633694,
             -0.202953396286476, 0.786617208861492, -0.583805982901842, 0.91666360939369, -0.602135912772221,
             0.503807046950863, -0.196264987447976, -0.565579687152807, 0.540878947793462, 0.183666358669345,
             -0.303576255198908, -0.896405440407756, -0.101939801890135, -0.049819872322279, 0.434240825173134,
             0.946552963504364, -0.32578927683003, -0.154255792477949, 0.577967633549953, -0.573697797217518])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        a = [z[i] - z[i + 1] for i in range(0, self.D - 1)]
        return np.sum([_z * _z for _z in a])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.prod([_z for _z in z])

        def g2(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([_z for _z in z])

        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return np.sum([_z * sin(4 * sqrt(abs(_z))) for _z in z])

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "g2":
                {
                    "name": "g2",
                    "func": g2,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-10, 10) for _ in range(0, self.D)
        ]


class C18(ConstrainedFunction):

    def __init__(self, dimensions):
        self.o = np.array(
            [-2.494401436611803, -0.306408781638572, -2.271946840536718, 0.381278325914122, 2.394875929583502,
             0.418708663782934, -2.082663588220074, 0.776060342716238, -0.374312845903175, 0.352372662321828,
             1.172942728375508, -0.24450210952894, 1.049793874089803, -1.716285448140795, -1.026167671845868,
             -1.223031642604231, 0.924946651665792, 0.93270056541258, -2.312880521655027, -0.671857644927313,
             -0.312276658254605, -0.973986111708943, -0.454151248193331, 2.420597958989111, 0.050346805172393,
             1.050203106200361, -0.05420584346617, -0.081533357726523, -0.968176219532845, 1.682281307624435])

        if dimensions > len(self.o):
            raise ValueError("Dimensions cannot be higher than o vector")

        super().__init__(dimensions)

    def evaluate(self, X):
        x = np.array(X)
        z = x - self.o[:self.D]
        a = [z[i] - z[i + 1] for i in range(0, self.D - 1)]
        return np.sum([_z * _z for _z in a])

    def constraints(self):
        def g1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return (1 / self.D) * np.sum([-_z * sin(sqrt(abs(_z))) for _z in z])

        def h1(X):
            x = np.array(X)
            z = x - self.o[:self.D]
            return (1 / self.D) * np.sum([_z * sin(sqrt(abs(_z))) for _z in z])

        return {
            "g1":
                {
                    "name": "g1",
                    "func": g1,
                    "op": operator.le,
                    "target": lambda x: 0
                },
            "h1":
                {
                    "name": "h1",
                    "func": h1,
                    "op": operator.eq,
                    "target": lambda x: 0
                }
        }

    def get_domain(self):
        return [
            (-50, 50) for _ in range(0, self.D)
        ]