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Travel the Same Path(TSP): A Novel TSP Solving Strategy

The paper can be found in here.

Running the Experiment

Data Generation

Both 01_generate_instances.py and 02_generate_dataset.py is for generating data. Please first set the parameters in parameters.py, where we specify all the data set parameter there.

01_generate_instances.py

This is to generate TSP instances. We formulate a random TSP problem by mixed linear programming and record it into instances_*.lp, where * refer to the number of the instance. To run this, use

python 01_generate_instances.py -n <tsp_size>

where <tsp_size> is 10, 15, 20, 25 in our experiments.

Note: In 01_generate_instances.py, we also generate some other instances with various <tsp_size> for transfer learning. Specifically, for an input <tsp_size>, we not only generate testing, training and validation data with <tsp_size>, we also generate small, medium, and large transfer data with size <tsp_size>/2, <tsp_size>/1.5, and <tsp_size>*2 respectively.

02_generate_dataset.py

After generating all the instances, we can create the dataset for imitation learning. This will record all the branching information when SCIPY solving an instance, and we'll use this information to learn the expert choice. To generate the dataset, we run

python 02_generate_dataset.py -p <probaility> -n <tsp_size> -j <workers>

These flags mean:

  1. -p: The probability for SCIPY asking for a strong branch. (Since this is un-common to ask for strong branch in every branching decision, and the original paper use 0.05. Here, we use 0.5 instead.)
  2. -n: TSP size. In our experiment, we train on TSP10 and TSP15 by using imitation learning.
  3. -j: Number of parallel jobs when creating data sample for one instance. This depends on your cpu power.

After running this, the data set should look like the following

---Travel-the-Same-Path
 |---data
 |     |-tsp15
 |     |    |-instances
 |     |    |   |-test
 |     |    |   |-train
 |     |    |   |-valid
 |     |    |-samples
 |     |        |-train
 |     |        |-valid
 |     |-tsp20
 |     |-tsp25
 |--- ...
 .
 .
 .

Training

03_train_gnn_imitation.py

We are now ready to train. We first use imitation learning. To run the script, use

python 03_train_gnn_imitation.py -n <tsp_size> -g <GPU id>

where -n is the size you want to train on, and -g is to specify the GPU id used by pytorch with CUDA.

04_evaluate.py

To evaluate our model performance, we use the following command.

python 05_evaluate.py -n <tsp_size> -g <GPU id> -l <imitation size>

where -n, -g are the same flag as we saw, and -l is to specify which model we want to evaluate. For example, if we want to evaluate the model which is only trained by imitation learning on <im_tsp_size>, then the argument of -l should be <im_tsp_size> namely the size of tsp instances the model you want to load is trained on.

Note

Firstly, the error detection of argument is not implemented, if you pass in things like -n -100, some weird things will happen...