https://github.com/bjam24/traveling-salesman-problem

The project is about solving symmetrical traveling salesman problem. The repository contains 4 optimization algorithms: Tabu Search, Hill Climbing with Multi-Start, Nearest Neighbor and Simulated Annealing.
https://github.com/bjam24/traveling-salesman-problem

algorithm automation hill-climbing nearest-neighbor python simulated-annealing tabu-search travelling-salesman-problem xlsx

Last synced: 8 days ago
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The project is about solving symmetrical traveling salesman problem. The repository contains 4 optimization algorithms: Tabu Search, Hill Climbing with Multi-Start, Nearest Neighbor and Simulated Annealing.

• Host: GitHub
• URL: https://github.com/bjam24/traveling-salesman-problem
• Owner: bjam24
• License: mit
• Created: 2021-11-14T21:36:00.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-10-03T20:30:51.000Z (4 months ago)
• Last Synced: 2024-12-01T21:12:27.568Z (2 months ago)
• Topics: algorithm, automation, hill-climbing, nearest-neighbor, python, simulated-annealing, tabu-search, travelling-salesman-problem, xlsx
• Language: Python
• Homepage:
• Size: 101 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Traveling-Salesman-Problem

## Description

This project was developed for the Computational Intelligence course at AGH University of Science and Technology during the 2021/2022 academic year. It features four custom-designed algorithms created from scratch in **Python** that are applied to **Symmetrical Traveling Salesman Problem (TSP)**. The project's structure and code have been refactored for clarity and efficiency. The algorithms showcased here are frequently integrated with **Machine Learning** algorithms.

## Algorithms

The presented results are intended for demonstration purposes only. Finding the optimal solution requires sufficient time and careful tuning of the parameters.

**Hill Climbing**







**Nearest Neighbour**







**Tabu Search**







**Simulated Annealing**







## Technology stack

- Python