Evolutionary multimodal optimization using the principle of locality

Handle URI:
http://hdl.handle.net/10754/562233
Title:
Evolutionary multimodal optimization using the principle of locality
Authors:
Wong, Kachun; Wu, Chunho; Mok, Ricky; Peng, Chengbin ( 0000-0002-7445-2638 ) ; Zhang, Zhaolei
Abstract:
The principle of locality is one of the most widely used concepts in designing computing systems. To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization. Instead of generating trial vectors randomly, the first method proposed takes advantage of spatial locality to generate trial vectors. Temporal locality is also adopted to help generate offspring in the second method proposed. Temporal and spatial locality are then applied together in the third method proposed. Numerical experiments are conducted to compare the proposed methods with the state-of-the-art methods on benchmark functions. Experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality. Further experiments are also conducted on two application problems. One is the varied-line-spacing holographic grating design problem, while the other is the protein structure prediction problem. The numerical results demonstrate the effectiveness of the methods proposed. © 2012 Elsevier Inc. All rights reserved.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Elsevier
Journal:
Information Sciences
Issue Date:
Jul-2012
DOI:
10.1016/j.ins.2011.12.016
Type:
Article
ISSN:
00200255
Sponsors:
The authors would like to express their deep gratitudes to the anonymous reviewers for their constructive comments. The authors would also like to thank Ling Qing for his source codes and insightful discussions. Last but not least, the authors would like to thank Kwong-Sak Leung and Man-Hon Wong for their contributions. ZZ acknowledges funding support from a NSERC Discovery Grant (RGPIN 327612-09).
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWong, Kachunen
dc.contributor.authorWu, Chunhoen
dc.contributor.authorMok, Rickyen
dc.contributor.authorPeng, Chengbinen
dc.contributor.authorZhang, Zhaoleien
dc.date.accessioned2015-08-03T09:57:22Zen
dc.date.available2015-08-03T09:57:22Zen
dc.date.issued2012-07en
dc.identifier.issn00200255en
dc.identifier.doi10.1016/j.ins.2011.12.016en
dc.identifier.urihttp://hdl.handle.net/10754/562233en
dc.description.abstractThe principle of locality is one of the most widely used concepts in designing computing systems. To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization. Instead of generating trial vectors randomly, the first method proposed takes advantage of spatial locality to generate trial vectors. Temporal locality is also adopted to help generate offspring in the second method proposed. Temporal and spatial locality are then applied together in the third method proposed. Numerical experiments are conducted to compare the proposed methods with the state-of-the-art methods on benchmark functions. Experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality. Further experiments are also conducted on two application problems. One is the varied-line-spacing holographic grating design problem, while the other is the protein structure prediction problem. The numerical results demonstrate the effectiveness of the methods proposed. © 2012 Elsevier Inc. All rights reserved.en
dc.description.sponsorshipThe authors would like to express their deep gratitudes to the anonymous reviewers for their constructive comments. The authors would also like to thank Ling Qing for his source codes and insightful discussions. Last but not least, the authors would like to thank Kwong-Sak Leung and Man-Hon Wong for their contributions. ZZ acknowledges funding support from a NSERC Discovery Grant (RGPIN 327612-09).en
dc.publisherElsevieren
dc.subjectCrowding differential evolutionen
dc.subjectEvolutionary Computationen
dc.subjectMultimodal optimizationen
dc.subjectNichingen
dc.subjectOptimizationen
dc.subjectSpatial localityen
dc.subjectTemporal localityen
dc.titleEvolutionary multimodal optimization using the principle of localityen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalInformation Sciencesen
dc.contributor.institutionDepartment of Computer Science, University of Toronto, Toronto, ON, Canadaen
dc.contributor.institutionTerrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canadaen
dc.contributor.institutionDepartment of ISE, Hong Kong Polytechnic University, Hung Hom, Hong Kongen
dc.contributor.institutionDepartment of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kongen
dc.contributor.institutionBanting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canadaen
dc.contributor.institutionDepartment of Molecular Genetics, University of Toronto, Toronto, ON, Canadaen
kaust.authorPeng, Chengbinen
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