Particle Swarm Optimization with an Aging Leader and Challengers
Particle Swarm Optimization with an Aging Leader and Challengers
复制标题
老龄化领导者和挑战者的粒子群优化
DOI：
10.1109/tevc.2011.2173577
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发表时间：
2013-04-01
影响因子：
14.3
通讯作者：
Shi, Yu-Hui
中科院分区：
文献类型：
--
作者：
Chen, Wei-Neng;Zhang, Jun;Shi, Yu-Hui
在自然界中，几乎每一种有机体都会衰老，寿命有限。生物学家将衰老作为维持生物多样性的一种重要机制。在群居的动物群体中，衰老使群体的老领导者变得软弱，为其他个体提供了挑战领导地位的机会。受这一自然现象的启发，将老化机制移植到粒子群算法(PSO)中，提出了一种具有老化引领者和挑战者的粒子群算法(ALC-PSO)。ALC-PSO算法在不显著影响PSO算法快速收敛特性的前提下，克服了早熟收敛问题。它的特点是为群体中的领导者分配一个不断增长的年龄和寿命，并允许其他个体在领导者变老时挑战领导权。领导者的寿命根据领导者的领导权力进行自适应调整。如果一位领导者表现出强大的领导能力，它就会活得更长，从而吸引一大群人走向更好的职位。否则，如果一位领导者未能改善群体并变老，新的粒子就会出现，挑战并夺取领导权，这就带来了多样性。如此一来，ALC-PSO算法中的“老龄化”概念实际上是一种具有挑战性的机制，可以促进一个合适的领导者来领导群体。该算法在17个基准函数上进行了实验验证。通过与八种流行的粒子群算法的比较，证实了该算法的高性能。
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.