GeneticAlgorithmTSP

Genetic algorithm code for solving Travelling Salesman Problem

Programming Language : Python

Number of cities : 11

General flow of solving a problem using Genetic Algorithm

            {
              initialize population;
              evaluate population;
              while TerminationCriteriaNotSatisfied
                  {
                    select parents for reproduction;
                    perform recombination and mutation;
                    evaluate population;
                  }
            }

run the following lines in terminal before proceeding :

pip install -r requirements.txt
conda install basemap
or run bash setup.sh (if running a linux based system)

pass this data frame to the genetic algorithm function as dist_mat

data = pd.read_csv("data/cities_and_distances.csv")
data.reset_index(inplace=True)
data1 = data.iloc[:,2:]
data1.index = data1.columns.values
data1

Initialize population: The initial population is a set of random routes generated using numpy.

Evaluate population: The evaluation is a process of finding how good the solutions is. This is

Mutation:

Crossover: Implemented PMX by goldberg - https://www.hindawi.com/journals/cin/2017/7430125/

OverallRun:

      ga_obj = GeneticAlgoLibrary.OverallGaRun(noverall=1,
                                     number_of_cities=11,
                                     initial_pop_size=1000,
                                     nelite=10,
                                     percentage_to_crossover=20,
                                     percentage_to_mutate=20,
                                     dist_mat=data1)

If you want to run the genetic algorithm with multiple runs

  for i in [10,100,1000]:
      ga_obj = GeneticAlgoLibrary.OverallGaRun(noverall=i,
                                           number_of_cities=11,
                                           initial_pop_size=10000,
                                           nelite=10,
                                           percentage_to_crossover=20,
                                           percentage_to_mutate=20,
                                           dist_mat=data1)
      ga_obj.run_overall_ga()

The solution obtained from running Genetic algorithm

Starting:

Final solution plotted with folium

Note:

1. The final solution was obtained after multiple runs of the Genetic Algorithm with different inital population sizes and overall runs.

_2. The map needs access to city_lat_lon data, in the code to the file that is availabe in the data folder, if you are running the code from a different folder, the path should be changed accordingly.

_3. basemap is now deprecated, will update the plots with new code when I find a package which can generate these kind of plots, if you have any suggestions, please forward them to Chaithanya Kumar

Update: Added plot support with folium and branca, updated the requirements file and lat lon file with required format data.

Update 20-04-2021: Fitness value calculation is now using ray multiprocessing to speed up the process.

sck22 / geneticalgorithmtsp Goto Github PK