Random-Search-for-Optimization

Random search (RS) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized. This repository contains the implementation in Python of various optimization algorithms:

• Linear Regression with Simulated Annealing
• Particle Swarm Optimization for Linear Regression
• TSP using Genetic Algorithm
• TSP using Nearest Neighbor Algorithm
• TSP using Simulated Annealing Algorithm

Pre-Installed Packages

The following packages need to be pre-installed before running the code:

1. gifsicle: To create animated GIFs of the optimization process.

sudo apt install gifsicle

2. imagemagick: To create an image of the optimization process.

sudo apt install imagemagick

How to Use

Clone the repository

git clone https://github.com/<username>/Random-Search-for-Optimization.git

Navigate to the directory

cd Random-Search-for-Optimization

Install the required packages

pip install -r requirements.txt

Run the Python scripts

python linear_regression_simulated_annealing.py

python linear_regression_particle_swarm_optimization.py

python tsp_genetic_algorithm.py

python tsp_nearest_neighbor_algorithm.py

python tsp_simulated_annealing_algorithm.py

Examples

Here's an example of running the TSP using Simulated Annealing Algorithm:

Navigate to the directory

cd Random-Search-for-Optimization

Run the Python script

python tsp_simulated_annealing_algorithm.py

The output: