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Developer Guide

Overview

This guide is intended for developers who want to understand, modify, or extend the GA-PSO hybrid optimization algorithm.

Code Structure

  • main_script.m: Contains the main loop for the GA-PSO algorithm and the initialization of parameters.

Function Descriptions

Initialization Functions

  • init_population: Initializes the population for the genetic algorithm.
  • init_pso: Initializes the particles for the PSO.

Genetic Algorithm Functions

  • roulette_wheel_selection: Selects parents based on their fitness.
  • crossover: Performs crossover between two parents to produce offspring.
  • mutation: Mutates the offspring with a given probability.

Particle Swarm Optimization Functions

  • evaluate_fitness: Evaluates the fitness of particles.
  • update_pso: Updates the position of particles.

Fitness Calculation

  • calculate_total: Calculates the total value based on the input variables.
  • calculate_fitness: Calculates the fitness based on the total value.

Using Custom Functions

To use your own functions for optimization:

  1. Define Custom Calculation Functions:

    • Update the calculate_total function to use your custom formula or logic.
    • Modify the calculate_fitness function to calculate the fitness based on your specific criteria.

  2. Example Customization:

function total = calculate_total(variables, constant)
    % Replace this with your custom logic
    total = sum(variables) + constant;
end

function fitness = calculate_fitness(total)
    % Replace this with your custom fitness logic
    fitness = 1 / (1 + abs(total - target));
end

  1. Adapt the main_script.m:

    • Ensure gen_count, upper_bounds, and lower_bounds are set according to the number of variables your custom functions require.

  2. Testing: Run the script and verify the results based on your specific problem.