Triggered Memory-Based Swarm Optimization in Dynamic Environments

Authors:
Hongfeng Wang;Dingwei Wang;Shengxiang Yang
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang 110004, P.R. China;School of Information Science and Engineering, Northeastern University, Shenyang 110004, P.R. China;Department of Computer Science, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom
Venue:
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Year:
2009

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Abstract

In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems.