Brunel University Research Archive (BURA) >
College of Engineering, Design and Physical Sciences >
Dept of Electronic and Computer Engineering >
Dept of Electronic and Computer Engineering Research Papers >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1121

Title: Evolving dynamic multiple-objective optimization problems with objective replacement
Authors: Guan, SU
Chen, Q
Mo, W
Keywords: Multi-objective genetic algorithms
Multi-objective problems
Multi-objective optimization
Non-stationary environment
Publication Date: 2005
Publisher: Springer
Citation: Artificial Intelligence Review 23(3): 267-293, May 2005
Abstract: This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.
URI: http://bura.brunel.ac.uk/handle/2438/1121
ISSN: 0269-2821
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

Files in This Item:

File Description SizeFormat
Evolving Dynamic Multi-objective Optimization Problems with Objective Replacement.pdf1.42 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.