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
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%20cost%20among%20all%20generations.png){kind=link}
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 ):
