Particle Swarm Optimization Algorithm with Compression Factor

This repository includes the following PSO algorithm implementations: - PSO with Compression Factor: Implements velocity clamping using constriction coefficients to ensure convergence - Weight-Improved Particle Swarm Algorithm: Features dynamically adjusted inertia weights for better balance between exploration and exploitation - Adaptive Weight Method: Automatically adjusts weights based on fitness landscape and search progress - Random Weight Method: Incorporates stochastic weight assignment to enhance search diversity - PSO with Variable Learning Factors: Dynamically adjusts cognitive and social learning parameters during optimization - Asynchronously Changing Learning Factors: Implements time-variant learning coefficients that change independently - Second-Order Particle Swarm Algorithm: Extends basic PSO with second-order dynamics for improved convergence - Second-Order Oscillatory Particle Swarm Algorithm: Combines oscillation characteristics with second-order particle movement - Chaotic Particle Swarm Algorithm: Integrates chaotic sequences for population initialization and parameter control - Hybrid Particle Swarm Algorithm: Combines PSO with other optimization techniques for enhanced performance - Crossover Particle Swarm Algorithm: Incorporates genetic algorithm crossover operations into PSO framework - Simulated Annealing Algorithm: Includes standard SA implementation as a comparative optimization method These implementations represent important variants and improvements to the standard Particle Swarm Optimization algorithm, covering various enhancement strategies and hybrid approaches commonly used in optimization research. Each algorithm includes proper parameter tuning and convergence mechanisms suitable for different problem domains.