Particle swarm optimization with an aging leader and challengers

Wei Neng Chen, Jun Zhang, Ying Lin, Ni Chen, Zhi Hui Zhan, Henry Shu Hung Chung, Yun Li, Yu Hui Shi

Research output: Contribution to journalArticle

358 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6151121
Pages (from-to)241-258
Number of pages18
JournalIEEE Transactions on Evolutionary Computation
Volume17
Issue number2
DOIs
Publication statusPublished - 8 Apr 2013

Keywords

  • aging
  • global search
  • leader
  • particle swarm optimization (PSO)
  • premature convergence

Fingerprint Dive into the research topics of 'Particle swarm optimization with an aging leader and challengers'. Together they form a unique fingerprint.

Cite this