Solving-TSP-with-Evolutionary-Genetic-Algorithm-Heuristic-Python

solving TSP (traveling sales man) using genetic algorithm in python
using heuristic algorithm to solve high dimensional tsp problem
Cities are included in "Cities List.txt" in repo to add or remove cities you've got to include or exclude cities in every line like: "1 909 649"
First number is city index and next two numbers are city euclidean coordinates: x,y which are set to 0 to 1000 but you can change the range to any range The distance between cities are calculated by Euclidean Distance which is:

I look forward to you questions about the project!
sample route found by algorithm(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 too 1000 ):

Average plot of all generations:
All best route of genereations

route at iteration of 0(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 20(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 40(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 60(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 80(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 100(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 120(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 140(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 160(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 180(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
Average chromosome(routes) costs among all generations.png		Average chromosome(routes) costs among all generations.png
Best solution found at 0 iteration.png		Best solution found at 0 iteration.png
Best solution found at 100 iterations.png		Best solution found at 100 iterations.png
Best solution found at 120 iterations.png		Best solution found at 120 iterations.png
Best solution found at 140 iterations.png		Best solution found at 140 iterations.png
Best solution found at 160 iterations.png		Best solution found at 160 iterations.png
Best solution found at 180 iterations.png		Best solution found at 180 iterations.png
Best solution found at 20 iterations.png		Best solution found at 20 iterations.png
Best solution found at 40 iterations.png		Best solution found at 40 iterations.png
Best solution found at 60 iterations.png		Best solution found at 60 iterations.png
Best solution found at 80 iterations.png		Best solution found at 80 iterations.png
Cities List.txt		Cities List.txt
Elite chromosome(routes) cost among all generations.png		Elite chromosome(routes) cost among all generations.png
README.md		README.md
TSP Genetic solver.py		TSP Genetic solver.py

MohammadAsadolahi/Solving-TSP-with-Evolutionary-Genetic-Algorithm-Heuristic-Python

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Solving-TSP-with-Evolutionary-Genetic-Algorithm-Heuristic-Python

route at iteration of 0(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 20(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 40(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 60(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 80(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 100(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 120(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 140(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 160(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

route found by algorithm at iteration of 180(feeding algorithm with list of 20 city euclidean coordinates: x,y from 0 to 1000 ):

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