Enhancing Swarm Intelligence: A Technical Evaluation of Particle Swarm Optimization and Its Chaotic Variant

Abstract

Optimization plays a vital role in solving complex real-world problems across engineering, data science, and computational intelligence. Particle Swarm Optimization (PSO), inspired by social behavior of bird flocking, has emerged as a powerful population-based optimization technique. However, PSO suffers from limitations such as premature convergence and stagnation in local optima. To overcome these challenges, Chaotic Particle Swarm Optimization (CPSO) integrates chaos theory into the PSO framework to enhance exploration and convergence characteristics. This paper presents a critical analytical comparison between PSO and CPSO in terms of algorithmic structure, convergence behavior, computational complexity, and performance efficiency. The study highlights how chaos-based enhancements improve optimization outcomes while also introducing additional computational considerations.