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User Guide

Overview

This guide will help you understand how to use the GA-PSO hybrid optimization algorithm implemented in MATLAB. This algorithm combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize a set of parameters.

Getting Started

Prerequisites

  • MATLAB installed on your computer.
  • Basic knowledge of MATLAB and optimization algorithms.

Files Overview

  • main_script.m: The main script that runs the GA-PSO optimization.

Running the Algorithm

  1. Open MATLAB.
  2. Load the script main_script.m.
  3. Modify the parameters at the top of the script as needed.
  4. Run the script.

Parameters Description

  • max_iterations: Maximum number of iterations for the optimization.
  • population_size: Number of individuals/particles in the population.
  • gen_count: Number of genes/variables to optimize.
  • crossover_prob: Probability of crossover in GA.
  • mutation_prob: Probability of mutation in GA.
  • upper_bounds: Array of upper bounds for each variable.
  • lower_bounds: Array of lower bounds for each variable.
  • w: Inertia weight for PSO.
  • c1: Cognitive coefficient for PSO.
  • c2: Social coefficient for PSO.

Using Custom Fitness Functions

To optimize the genes using your own functions, follow these steps:

  1. Define Your Custom Functions: Modify the calculate_total and calculate_fitness functions to reflect your specific optimization problem.

    Example:

function total = calculate_total(variables, constant)
    % Your custom calculation here
    total = custom_calculation(variables, constant);
end

function fitness = calculate_fitness(total)
    % Your custom fitness calculation here
    fitness = custom_fitness_calculation(total);
end

  1. Update main_script.m: Ensure that the gen_count, upper_bounds, and lower_bounds parameters match the number of variables in your custom functions.

  2. Run the Algorithm: Execute the script with your custom-defined functions to optimize the parameters according to your problem.