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Travelling Salesman Genetic AI Final

Giới thiệu

Đồ án kết thúc môn học môn Trí tuệ nhân tạo, chủ đề là áp dụng thuật toán di truyền giải bài toán người du lịch (Travelling salesman problem)

Cách cài đặt

  • Bước 1: Tải git và tạo tài khoản
  • Bước 2: Copy link mã nguồn https://github.com/ThienNguyen3001/Travelling-Salesman-Genetic-AI-Final.git
  • Bước 3: Mở git bash và clone về máy
  • Bước 4: Tải thư viện Numpy, Tkinter và Matplotlib về máy
pip install numpy
pip install matplotlib
pip install tk
  • Bước 5:
    • Trong thư mục src chứa code thuật toán.
    • Trong thư mục có các file Experiment có đuôi .ipynb là các file thí nghiệm của thành viên trong nhóm.
    • File Demo.py là file chạy giao diện, mở file lên và chạy chương trình.

Thành viên

Tên Github
Nguyễn Ngọc Thiện Thiện Nguyễn
Nguyễn Minh Nhựt Nhựt Nguyễn
Nguyễn Trọng Hưởng Arthur Nguyen
Trần Viết Gia Huy Tommyhuy1705

Lời cảm ơn

Chúng em xin cảm ơn TS. Đặng Ngọc Hoàng Thành đã giúp đỡ chúng em về kiến thức, dìu dắt chúng em để hoàn thành phần dự án này. Và cũng xin cảm ơn các thành viên nhóm đã đóng góp tích cực vào dự án để được hoàn thành trọn vẹn.

Tham khảo

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  3. Goyal, S. (2010). A Survey on Travelling Salesman Problem. https://micsymposium.org/mics_2010_proceedings/mics2010_submission_51.pdf
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  7. TSP-Data for the Traveling Salesperson Problem. (2019). Fsu.edu. https://people.sc.fsu.edu/~jburkardt/datasets/tsp/tsp.html
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  11. Martin, O., Otto, S., & Felten, E. (1991). Large-Step Markov Chains for the Traveling Salesman Problem (pp. 299–326). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=2ebfc1496d19d10bc8f9cfcf7a540ce93f7796f2
  12. Kirkpatrick, S. (1984). Optimization by simulated annealing: Quantitative studies. Journal of Statistical Physics, 34(5-6), 975–986. https://doi.org/10.1007/bf01009452
  13. Holland, J. (1975). Adaptation in Natural and Artificial Systems. In ADAPTATION iN NATURAL AND ARTIFICIAL SYSTEMS. https://books.google.com.vn/books?hl=vi&lr=&id=5EgGaBkwvWcC&oi=fnd&pg=PR7&dq=+Adaptation+in+Natural+and+Artificial+Systems+Holland+1975+University+of+Michigan+Press&ots=mKik04Gotm&sig=jqDlXMrrPG_X38yFO6vPgGl04nA&redir_esc=y#v=onepage&q=Adaptation%20in%20Natural%20and%20Artificial%20Systems%20Holland%201975%20University%20of%20Michigan%20Press&f=false
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  15. Ahn, C. W., & Ramakrishna, R. S. (2003). Elitism-based compact genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(4), 367–385. https://doi.org/10.1109/tevc.2003.814633
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  19. Harifi, S., & Mohamaddoust, R. (2023). Zigzag mutation: a new mutation operator to improve the genetic algorithm. Multimedia Tools and Applications, 82(29), 45411–45432. https://doi.org/10.1007/s11042-023-15518-3
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