Enhanced PSO algorithm (Includes 3 M-files and documentation)

This article presents an enhanced Particle Swarm Optimization (PSO) algorithm package comprising three MATLAB M-files and accompanying documentation. The implementation specifically incorporates the groundbreaking improvements proposed by Clerc and Kennedy in 2002, which introduced critical parameter tuning methodologies and convergence guarantees. The algorithmic enhancements significantly boost optimization performance through improved velocity update mechanisms and intelligent parameter control strategies, enabling more effective global optimum exploration across complex search spaces. The refined PSO algorithm demonstrates superior performance in solving diverse optimization problems, combining Clerc and Kennedy's foundational work with subsequent research advancements. Key implementation features include adaptive inertia weight adjustment and constriction coefficient integration within the velocity update equation: v(t+1) = χ × [v(t) + φ₁ × rand() × (pbest - x(t)) + φ₂ × rand() × (gbest - x(t))]. This mathematical formulation ensures balanced exploration-exploitation trade-offs and prevents premature convergence. This enhanced PSO variant has found extensive applications across multiple domains including engineering design optimization, economic modeling, and computational intelligence systems. Through systematic parameter control and improved swarm intelligence mechanisms, practitioners can achieve superior solution quality and accelerated convergence rates when addressing complex real-world optimization challenges. The modular code structure permits straightforward integration with existing optimization frameworks while maintaining computational efficiency.