Improved Particle Swarm Optimization Using the Cauchy Criterion

Abstract

 Particle Swarm Optimization (PSO) is a popular metaheuristic algorithm inspired by the social behavior of bird flocking. Despite its effectiveness, PSO suffers from premature convergence and limited exploration capability in high-dimensional search spaces. To address these issues, researchers have proposed various enhancements to the standard PSO algorithm. One such enhancement is the utilization of the Cauchy criterion, which introduces heavy-tailed random movements into the particle updates. This review paper provides an overview of the Cauchy-based enhancements in PSO algorithms, highlighting their advantages, implementation strategies, and applications across diverse optimization problems.