Particle Swarm Optimization with an Aging Leader and Challengers

Chen, WN;Zhang, J;Lin, Y;Chen, N;Zhan, ZH;Chung, HSH;Li, Y;Shi, YH

[Chen, Wei-Neng; Zhang, Jun; Lin, Ying; Chen, Ni; Zhan, Zhi-Hui] Sun Yat Sen Univ, Dept Comp Sci, Key Lab Machine Intelligence & Sensor Network, Minist Educ, Guangzhou 510275, Guangdong, Peoples R China.
[Chung, Henry Shu-Hung] City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China.
[Li, Yun] Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland.
[Shi, Yu-Hui] Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou 215123, Peoples R China.


Volume:17 Issue:2Pages:241-258


Publication Year:2013




Latest Impact Factor:11.554

Document Type:Journal Article


In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept "aging" in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.


Aging global search leader particle swarm optimization (PSO) premature convergence

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