Travelling Salesman problem with full source code in Python | TSP project.ZIP

Travelling Salesman problem with full source code in Python | TSP project

Firstly, this worksheet is one of the worksheets from which your laboratory worksheets portfolio of work will be assessed.
This worksheet is about empirically evaluating how scalable a number of single population heuristic search methods are at solving the Travelling Salesman Problem (TSP) when applied to a number of different sized problems. The aim is to evaluate and demonstrate how well each of the methods performs as the size of the problem increases.
The overall objective of this worksheet is to produce; present and report on, a Java program that is capable of solutions the TSP on a number of different sized problems using a number of different heuristic search algorithms (see below).

Solving the TSP Problem
The Travelling Salesman problem (TSP) has been described fully in the lectures. The purpose of this worksheet is to:
1) Implement a number of the algorithms (listed below) to solve the TSP
2) Compare the algorithms on a number of different sized datasets
3) Report on the accuracy of the methods as the problem size changes

You should try and implement more than one out of the four following methods:
1) Simple Hill Climbing (Random Mutation Hill Climbing)
2) Stochastic Hill Climber
3) Random Restart Hill Climber
4) Simulated Annealing
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