MATLAB Implementation of Enhanced Particle Swarm Optimization Algorithm

The Enhanced Particle Swarm Optimization (PSO) algorithm is a population-based intelligent optimization method. When implemented in MATLAB, its performance can be improved through parameter adjustments and the introduction of new strategies. Traditional PSO algorithms tend to fall into local optima and exhibit slow convergence rates, while the enhanced version achieves better balance between global search capability and convergence speed.

Key improvement approaches include the following aspects: Inertia Weight Adjustment: Dynamically modify inertia weights to maintain larger values during initial iterations for enhanced global exploration, then gradually decrease them in later stages to improve local search precision. In MATLAB, this can be implemented using linear or nonlinear decreasing functions like w = w_max - (w_max-w_min)*iter/max_iter. Learning Factor Optimization: Introduce adaptive learning factors that allow individual and social learning capabilities to dynamically change across different iteration phases, balancing exploration and exploitation. Code implementation may involve designing time-varying functions for cognitive (c1) and social (c2) parameters. Velocity Limiting: Properly set velocity boundaries to prevent particles from moving too rapidly and overshooting optimal solutions. MATLAB implementations typically use v_max = k*(x_max-x_min) with k∈[0.1,0.2] to constrain particle movements. Hybrid Strategies: Incorporate concepts from other optimization algorithms (e.g., genetic algorithms, differential evolution) by introducing mutation or crossover mechanisms to enhance population diversity. This can be coded through conditional operations that apply genetic operators when stagnation is detected.

Through these enhancements, the MATLAB-implemented PSO algorithm demonstrates faster convergence rates and stronger global search capabilities in optimization problems, making it suitable for complex engineering optimization, machine learning parameter tuning, and similar applications.