Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer
Yu‐Jun Zheng, Hai-Feng Ling, Qiu Guan · Mathematical Problems in Engineering · 2012
Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self‐adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.