A Novel Particle Swarm Optimizer with Randomly Occurring Uncertainty

Abstract

Particle Swarm Optimization (PSO) has been widely applied due to its simplicity and effectiveness in solving optimization problems. However, PSO often suffers from premature convergence and stagnation in local optima, especially in complex search spaces. This paper proposes a novel PSO algorithm by incorporating stochastic perturbations into the velocity updating mechanism. Specifically, the acceleration coefficients are perturbed by the introduced randomly occurring uncertainty to improve the search ability of the entire swarm. Experimental results on representative CEC benchmark functions demonstrate that the proposed algorithm outperforms several existing PSO variants.