Dynamic particle population particle swarm optimization based on population age model
Shanhe Jiang, Zhicheng Ji · Systems Engineering - Theory & Practice · 2012
For the problem that particle swarm optimization(PSO) algorithm often suffers from being trapped in local optima so as to be premature convergence,a dynamic particle population PSO based on population age model is proposed to efficiently control premature stagnation.Firstly,life population age model is constructed,which divides diverse age group for a certain particle,and effectively regulates population size in accordance with population environment and individual information.Secondly,the particle reproduction strategy of the good particle for keeping the diversity of swarm and the particle vanishing strategy of the worst particle for decreasing excessive calculated amount are designed,so the optimal performance is guaranteed in this algorithm.Finally,the comparison experiments have been made with four benchmark functions between the proposed algorithm and other improved PSO.The simulation experimental results show that the proposed method not only greatly improves the global successful searching probability and searching efficiency,but also effectively avoids the local stagnation problem.