https://github.com/nikhil97353/evolutionary-algorithm-for-traveling-salesman-problem-tsp-

In this project, I implemented an Evolutionary Algorithm (EA) to solve the Travelling Salesman Problem (TSP), a classic optimization challenge where the goal is to find the shortest route that visits a set of cities exactly once and returns to the starting point.
https://github.com/nikhil97353/evolutionary-algorithm-for-traveling-salesman-problem-tsp-

crossover-operator evolutionary-algorithms genetic-algorithm shortest-path-routing-algorithm travelling-salesman-problem

Last synced: 4 months ago
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In this project, I implemented an Evolutionary Algorithm (EA) to solve the Travelling Salesman Problem (TSP), a classic optimization challenge where the goal is to find the shortest route that visits a set of cities exactly once and returns to the starting point.

• Host: GitHub
• URL: https://github.com/nikhil97353/evolutionary-algorithm-for-traveling-salesman-problem-tsp-
• Owner: nikhil97353
• License: mit
• Created: 2024-09-15T01:18:55.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-09-15T14:34:02.000Z (9 months ago)
• Last Synced: 2025-01-11T06:45:09.826Z (5 months ago)
• Topics: crossover-operator, evolutionary-algorithms, genetic-algorithm, shortest-path-routing-algorithm, travelling-salesman-problem
• Language: Python
• Homepage:
• Size: 169 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Evolutionary Algorithm for Traveling Salesman Problem (TSP) with Graph Visualization

This project implements an Evolutionary Algorithm (EA) to solve the Traveling Salesman Problem (TSP), utilizing XML files that contain graph representations of various city maps. The project also includes visualizations of the cities and their connections using NetworkX and Matplotlib. 

Features

Graph Parsing: Reads the XML file representing cities and distances between them and constructs a graph using NetworkX.

EA for TSP: Implements an evolutionary algorithm to find near-optimal solutions for the TSP, including improved initialization, tournament selection, crossover, and mutation.

Visualization: Graph visualizations are generated for the city maps, showing city connections and distances.

Convergence Curve: Plots the convergence curve to track the fitness (total travel cost) over iterations.

Scatter Plot: Generates scatter plots to observe how population size influences the solution's fitness.

Getting Started

Prerequisites

To run this project, you will need the following dependencies:

Python 3.x

Libraries: xml.etree.ElementTree, networkx, matplotlib, numpy

You can install all the required libraries via the requirements.txt file.