Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5865
Title: Triggered memory-based swarm optimization in dynamic environments
Authors: Wang, H
Wang, X
Yang, S
Issue Date: 2007
Publisher: Springer-Verlag
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
URI: http://www.springerlink.com/content/968170487738v217/?p=08562be785674584af3c691b82591045&pi=1
http://bura.brunel.ac.uk/handle/2438/5865
DOI: http://dx.doi.org/10.1007/978-3-540-71805-5_70
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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