GA-PSO Algorithm Implementation

This document presents relevant programs and implementations for the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) hybrid algorithm. We hope this information proves valuable for your research and applications!

The GA-PSO algorithm is an intelligent optimization technique based on swarm intelligence principles. It combines the evolutionary mechanisms of Genetic Algorithms (GA) with the social behavior simulation of Particle Swarm Optimization (PSO). The algorithm achieves multi-dimensional optimization by simulating particle movement and mutation in search space through key operations like position updates and velocity adjustments. This hybrid approach is widely implemented in function optimization, feature selection, image processing, and machine learning applications, often requiring carefully tuned parameters such as crossover rates, mutation probabilities, and inertia weights.

The core mechanism of GA-PSO involves coordinated optimization between individuals and the population. Individuals undergo continuous self-improvement through fundamental GA operations: selection (using methods like roulette wheel or tournament selection), crossover (implemented through single-point or uniform crossover), and mutation (applying random bit-flips or Gaussian mutations). Meanwhile, the population employs PSO principles by selecting global best solutions from all individuals and continuously updating particle positions using velocity update equations: v_i = w*v_i + c1*rand()*(pbest_i - x_i) + c2*rand()*(gbest - x_i), followed by position updates: x_i = x_i + v_i. This dual approach enables efficient global optimum search while maintaining population diversity.

We anticipate this enhanced explanation with implementation details will help you better understand the GA-PSO algorithm and achieve superior results in practical applications. Typical implementations involve initializing a population of particles with random positions and velocities, then iteratively applying GA operations and PSO updates until convergence criteria are met.