Εμφάνιση απλής εγγραφής

dc.creatorEpitropakis, M. G.en
dc.creatorTasoulis, D. K.en
dc.creatorPavlidis, N. G.en
dc.creatorPlagianakos, V. P.en
dc.creatorVrahatis, M. N.en
dc.date.accessioned2015-11-23T10:26:19Z
dc.date.available2015-11-23T10:26:19Z
dc.date.issued2011
dc.identifier10.1109/tevc.2010.2083670
dc.identifier.issn1089-778X
dc.identifier.urihttp://hdl.handle.net/11615/27373
dc.description.abstractDifferential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.en
dc.source.uri<Go to ISI>://WOS:000287671500006
dc.subjectAffinity matrixen
dc.subjectdifferential evolutionen
dc.subjectmutation operatoren
dc.subjectnearesten
dc.subjectneighborsen
dc.subjectGLOBAL OPTIMIZATIONen
dc.subjectALGORITHMSen
dc.subjectADAPTATIONen
dc.subjectDESIGNen
dc.subjectComputer Science, Artificial Intelligenceen
dc.subjectComputer Science, Theory &en
dc.subjectMethodsen
dc.titleEnhancing Differential Evolution Utilizing Proximity-Based Mutation Operatorsen
dc.typejournalArticleen


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