Qingya Sui

Aug 16, 2023 | onlinelibrary.wiley.com | Lin Zhong |Haotian Li |Qingya Sui |Jiatianyi Yu

Abstract Particle swarm optimization (PSO) is a popular optimization technique known for its simplicity and effectiveness. This paper introduces a variant that achieves a better balance between exploration and exploitation, named DiPSO. DiPSO incorporates a novel strategy based on trends in mean distance between individuals for local exploitation control. Experiments on 29 benchmark functions demonstrate that DiPSO consistently outperforms the state-of-the-art variant of PSO.