Master-Slave Optimization (MSO)

Rastrigin Function is tested

Explanation of the Code:

1. Initialization: The algorithm initializes a population of slave agents, distributing them randomly within the given bounds.
2. Parallel Evaluation (parfor): The parfor loop (from MATLAB’s Parallel Computing Toolbox) is used to distribute solution evaluations across multiple slave agents in parallel, speeding up the optimization process.
3. Solution Perturbation: Each slave agent generates a new solution by adding a small random perturbation to its current position and then evaluates the fitness of this new solution.
4. Master Node: Collects results from all slave agents, updates the global best solution, and manages the iteration process.
5. Objective Function: The Rastrigin function is used as a test case, but you can replace it with your own objective function.

Customization:

• Objective Function: Replace objFunc with your custom function to optimize.
• Parameters: Adjust the number of slave agents, iterations, and search space bounds as needed.
• Parallel Computing: Ensure you have the Parallel Computing Toolbox installed and configured to use parfor.

Note:

• The parfor loop can significantly speed up computations for problems that are expensive to evaluate. If you don’t have the Parallel Computing Toolbox, you can replace parfor with a regular for loop, but it will run sequentially.