https://github.com/joozef315/tsp-by-metaheuristics
This Python package provides implementations of three metaheuristic algorithms to solve the Traveling Salesman Problem (TSP): Steepest Ascent Hill Climbing, Simulated Annealing, and Ant Colony Optimization.
https://github.com/joozef315/tsp-by-metaheuristics
ant-colony-algorithm hill-climbing-algorithm metaheuristic-optimisation python simulated-annealing tsp-problem
Last synced: 4 months ago
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This Python package provides implementations of three metaheuristic algorithms to solve the Traveling Salesman Problem (TSP): Steepest Ascent Hill Climbing, Simulated Annealing, and Ant Colony Optimization.
- Host: GitHub
- URL: https://github.com/joozef315/tsp-by-metaheuristics
- Owner: JooZef315
- License: mit
- Created: 2024-05-12T14:40:42.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-15T13:31:43.000Z (about 1 year ago)
- Last Synced: 2025-01-17T04:46:22.092Z (5 months ago)
- Topics: ant-colony-algorithm, hill-climbing-algorithm, metaheuristic-optimisation, python, simulated-annealing, tsp-problem
- Language: Python
- Homepage: https://pypi.org/project/TSPbyMetaheuristics/1.1/
- Size: 79.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: License.txt
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README
# TSPbyMetaheuristics
## Traveling Salesman Problem Solver
## Description
This Python package provides implementations of three metaheuristic algorithms to solve the Traveling Salesman Problem (TSP): **Steepest Ascent Hill Climbing**, **Simulated Annealing**, and **Ant Colony Optimization**. The Traveling Salesman Problem is a classic problem in combinatorial optimization where the goal is to find the shortest possible route that visits each city exactly once and returns to the original city.
## Files
- `hillClimbing.py`: Implementation of the Steepest Ascent Hill Climbing algorithm for solving the TSP.
- `simulatedAnnealing.py`: Implementation of the Simulated Annealing algorithm for solving the TSP.
- `antColony.py`: Implementation of the Ant Colony Optimization algorithm for solving the TSP.
- `travelingSalesman.py`: Utility functions for reading TSP instances from files and other generic tasks.
## Features
- Solves the Traveling Salesman Problem using three different metaheuristic algorithms.
- Supports reading TSP instances from files in standard formats.
- Easy-to-use interface for running and comparing different algorithms.
## Installation
1. Clone the repository:
```bash
pip install TSPbyMetaheuristics
```
2. You're ready to use the package!
## Usage
1. Import the desired algorithm(s) from the package:
```python
from TSPbyMetaheuristics import hillClimbing, simulatedAnnealing, antColony
tspHC = hillClimbing(simulations= 100)
tspSA = simulatedAnnealing(T0= 100)
tspAC = antColony(genrations_num= 20, Beta= 5)
```
2. Read a TSP instance from a file using the provided utility functions:
```python
# file.txt should look something like this
city_index x_coordinate y_coordinate
0 500.0 300.0
```
```python
tspHC.read_routes_from_file('file.txt')
tspSA.read_routes_from_file('file.txt')
tspAC.read_routes_from_file('file.txt')
#OR
tspHC.read_routes_from_list([[x1, y1], [x2, y2]])
tspSA.read_routes_from_list([[x1, y1], [x2, y2]])
tspAC.read_routes_from_list([[x1, y1], [x2, y2]])
```
3. Apply the chosen algorithm(s) to solve the TSP:
```python
solution_hc, shortest_path_hc = tspHC.steepest_ascent()
solution_sa, shortest_path_sa = tspSA.simulated_annealing()
solution_ac, shortest_path_ac = tspAC.run_monte_carlo( simulations = 20)
```
4. Compare the solutions obtained and choose the best one for your problem.
For more details on the usage of each algorithm, refer to the documentation in their respective files.
## Example Output
## License
This project is licensed under the [MIT License](License.txt).