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|Title:||Triggered memory-based swarm optimization in dynamic environments|
|Citation:||EvoWorkshops 2007: Applications of Evolutionary Computing, 4448: 637 - 646, Jun 2007|
|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.|
|Description:||This is a post-print version of this article - Copyright @ 2007 Springer-Verlag|
|Appears in Collections:||Publications|
Dept of Computer Science Research Papers
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