Enhanced Particle Swarm Optimization: Adaptive PSO Algorithm

The Adaptive Particle Swarm Optimization (APSO) represents an advanced variant of the classic Particle Swarm Optimization algorithm. By integrating entropy measurements and average inter-particle distance calculations, APSO achieves substantially faster convergence rates compared to conventional implementations. The algorithm dynamically adjusts inertia weights and acceleration coefficients based on swarm diversity metrics, enabling more effective exploration of complex solution spaces while minimizing premature convergence to local optima. Key implementation features include: - Real-time entropy computation to monitor population diversity - Adaptive parameter tuning using particle distribution statistics - Distance-based topological adjustments for neighborhood structures - Fitness-guided search direction modification mechanisms This enhanced approach maintains the fundamental PSO velocity and position update equations while introducing intelligent feedback loops that automatically balance exploration and exploitation phases during optimization.