README.md

The code of course project in Advanced Artificial Intelligence

• This is the code of course project in Advanced Artificial Intelligence.This project uses NSGA-II to solve the problem of Multiple Traveling Salesmen Problem.
• The baseline code is from https://github.com/Anupal/GA-for-mTSP ,we have do some adjust to make it more suitable for our experiment.
• The population select algorithm in our improve was mainly based on the NSGA-II:https://github.com/haris989/NSGA-II; most of the code was based on the arifield's work:https://github.com/ariefield/MTSP-Genetic

Contributors

The names of contributors are not in particular order.

NAME	ID	CONTRIBUTION
Zhang Zhicheng(leader)	12132336	1/3
Chen Yuxiang	12132330	1/3
Lei Chenyang	12132375	1/3

Operating instructions

Repeate our experiment result

1. Change the dir to baseline/code/, and run the shell repeat_test.sh, you will get the baseline result saved in the baseline_run_data.json
   
   
2. Change the dir ro mtsp_nsga_ii/code/, and run the python file repeat_test.py, you will get the improved GA result saved in the ours_run_data.json
   
   
3. Move the two .json file you get in the previous step to summary_figure/.Run polt_figure.pyand summary.py ,you will get the result figure and table of this two algorithm we represent in our report.

Note: It needs a lot of time to run the experiment, as we repeat 30 times in each dataset.

Or just run the improve GA algorithm

Change the dir to mtsp_nsga_ii/code/

>python main.py 
usage main.py --problem [--traveller] [--population] [--generations] [--mutation]
optional arguments:
        --problem      problem name
        --traveller    number of travellers,default 5
        --population   number of population,default 100
        --generations  number of generations, default 200
        --mutation     nutation rate, default 0.2