https://github.com/2gpb/antcolonyoptimization
This repository contains an implementation of the Ant Colony Optimization (ACO) algorithm, a nature-inspired optimization technique used for solving combinatorial problems.
https://github.com/2gpb/antcolonyoptimization
algorithm metaheuristic-optimisation python
Last synced: 2 months ago
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This repository contains an implementation of the Ant Colony Optimization (ACO) algorithm, a nature-inspired optimization technique used for solving combinatorial problems.
- Host: GitHub
- URL: https://github.com/2gpb/antcolonyoptimization
- Owner: 2Gpb
- Created: 2025-01-29T13:25:50.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-29T22:04:45.000Z (4 months ago)
- Last Synced: 2025-02-09T07:17:26.735Z (4 months ago)
- Topics: algorithm, metaheuristic-optimisation, python
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Ant Colony Optimization (ACO)
This project implements the **Ant Colony Optimization (ACO)** algorithm, inspired by the natural foraging behavior of ants. ACO is a powerful metaheuristic optimization method used to solve complex combinatorial optimization problems, such as the Traveling Salesman Problem (TSP), by simulating the behavior of ants searching for food while leaving pheromone trails to communicate with other ants.
### Main Components
- **Ant Class** - Implements the properties and methods associated with the behavior of individual ants. This class handles the movement of ants, the construction of solutions, and the calculation of path lengths.
- **AntColonyOptimization Class** - Implements the core mechanics of the ACO algorithm. This class controls the iteration process, pheromone updates, and solution improvement.
### Getting Started:
To run this project, you need to install the required dependencies. You can install them using pip:
```
pip install -r requirements.txt
```
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# Visual
