Travelling Salesman Problem: playing with various optimisation algorithms
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Updated
Nov 9, 2021 - Jupyter Notebook
Travelling Salesman Problem: playing with various optimisation algorithms
Notebooks created for a college assignment where simulated annealing was used to solve the travelling salesman problem.
This repository contains a Python notebook implementing a class for solving multiple Traveling Salesman Problems (TSP) using Pyomo and the CPLEX solver. The class includes a solution for the simple TSP scenario when there is only one driver.
The travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?" In this notebook, I demonstate the solution of this problem with the genetic algorithm.
The travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?" In this notebook, I demonstate the solution of this problem with the simulated annealing algorithm.
Contains notebook implementations for the AI based assignments using graph based algorithms that are commonly used in solving AI based problems. Algorithms include BFS, DFS, Hill Climbing, Differential Evolution, Genetic, Back Tracking..
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