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dc.contributor.authorWang, H-
dc.contributor.authorWang, D-
dc.contributor.authorYang, S-
dc.identifier.citationSoft Computing, 13(8-9): 763 - 780, 2009en_US
dc.descriptionCopyright @ Springer-Verlag 2008en_US
dc.description.abstractDynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.en_US
dc.description.sponsorshipThis work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01.en_US
dc.subjectGenetic algorithmen_US
dc.subjectMemetic algorithmen_US
dc.subjectLocal searchen_US
dc.subjectCrossover-based hill climbingen_US
dc.subjectMutation-based hill climbingen_US
dc.subjectDual mappingen_US
dc.subjectTriggered random immigrantsen_US
dc.subjectDynamic optimization problemsen_US
dc.titleA memetic algorithm with adaptive hill climbing strategy for dynamic optimization problemsen_US
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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