Random Search Optimization Techniques

This repository contains implementations of three different random search optimization techniques for solving the Traveling Salesman Problem (TSP). Each technique is implemented in a separate Jupyter Notebook.

Techniques Implemented

1. Ant Colony Optimization Algorithm for TSP

   • File: Ant Colony Algorithm For TSP.ipynb
   • Description: This notebook demonstrates the Ant Colony Optimization (ACO) algorithm applied to the Traveling Salesman Problem (TSP). It includes an implementation of the ACO approach and an example using a TSP dataset.

2. Generational Genetic Algorithm for TSP

   • File: Genetic Algorithm For TSP.ipynb
   • Description: In this notebook, I explore the Generational Genetic Algorithm (GA) for solving the TSP. It covers the GA principles, crossover, mutation, and fitness evaluation. A TSP instance is used to showcase the algorithm’s performance.

3. Nearest Neighbor Algorithm for TSP

   • File: Nearest Neighbour Algorithm For TSP.ipynb
   • Description: The Nearest Neighbor Algorithm (NNA) is a simple heuristic for solving the TSP. This notebook walks through the NNA steps and provides an example using a TSP dataset.

Dataset: City Coordinates

The dataset provides the coordinates (x, y) for 15 cities. Each row corresponds to a city, and the columns represent the following information:

• City: A unique identifier for each city.
• x: The x-coordinate of the city.
• y: The y-coordinate of the city.

Feel free to explore how these coordinates relate to the optimization techniques implemented in the repository. If you have any specific questions or need further assistance, let me know! 😊🗺️