NathanaelTamirat / Ant-colony-optimization

Ant colony optimization (ACO) is a nature-inspired algorithm that can be used to solve the traveling salesman problem (TSP). The TSP is a combinatorial optimization problem that involves finding the shortest route between a set of cities, while visiting each city and returning to the starting city.

Ant Colony Optimization (ACO) for the Traveling Salesman Problem (TSP)

This project implements the Ant Colony Optimization (ACO) algorithm to solve the Traveling Salesman Problem (TSP) in Java. ACO is inspired by the foraging behavior of ants and is used for finding optimal paths through graphs.

Features

• ACO algorithm implementation
• Visualization of the TSP solution
• Configurable algorithm parameters
• Example dataset for demonstration

Requirements

• Java Development Kit (JDK) 8 or higher

Project Structure

ACO/
├── Ants/
│   ├── Ant.java
│   └── AntColonyOptimization.java
├── Display/
│   └── WindowTSP.java
├── Graph/
│   ├── Edge.java
│   ├── Graph.java
│   ├── Node.java
│   └── Vertex.java
├── IO/
│   ├── Import.java
│   └── bays29.tsp
├── Sample/
│   └── sample_run.gif
└── Main.java

Getting Started

1. Clone the repository

git clone https://github.com/NathanaelTamirat/Ant-colony-optimization.git

2. Compile the Project

javac Ants/*.java Display/*.java Graph/*.java IO/*.java Main.java

3. Run the Example

java Main

Graph Visualization

The WindowTSP class is used to visualize the TSP solution. When you run the Main class, a window will display the optimal tour found by the ACO algorithm.