A hierarchical simple particle swarm optimization with mean dimensional information
Publication date: Available online 8 January 2019
Source: Applied Soft Computing
Author(s): Hao-Ran Liu, Jing-Chuang Cui, Ze-Dan Lu, Da-Yan Liu, Yu-Jing Deng
To reduce the negative influence of the overemphasis of gbest the dimensional information of particle is introduced to be a new example. This additional information source is incorporated into simple PSO to establish a simpler position model. Another two simpler position updating models, cognition only model and social only model, based on the simple PSO algorithm are presented as well. Time hierarchy strategy is extended from probability hierarchy, both aiming to make full use of advantages of three models. Three models are used with time or probability hierarchy to update each particle’s position. Thus, two proposed algorithms THSPSO and PHSPSO are finally obtained. Experiments are conducted on fifteen benchmark functions. The results demonstrate the two proposed algorithms both have excellent performances for basic functions compared with other popular PSO variants. Probability hierarchy strategy is more effective than time hierarchy strategy in general.
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