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Enhanced differential evolution hybrid scatter search for discrete optimization

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dc.title Enhanced differential evolution hybrid scatter search for discrete optimization en
dc.contributor.author Davendra, Donald David
dc.contributor.author Onwubolu, Godfrey
dc.relation.ispartof 2007 IEEE Congress on Evolutionary Computation, Vols 1-10, Proceedings
dc.identifier.isbn 978-1-4244-1339-3
dc.date.issued 2007
dc.citation.spage 1156
dc.citation.epage 1162
dc.event.title IEEE Congress on Evolutionary Computation
dc.event.location Singapore
utb.event.state-en Singapore
utb.event.state-cs Singapur
dc.event.sdate 2007-09-25
dc.event.edate 2007-09-28
dc.type conferenceObject
dc.language.iso en
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en
dc.identifier.doi 10.1109/CEC.2007.4424600
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4424600
dc.description.abstract A hybrid approach of the enhanced differential evolution (EDE) and scatter search (SS), termed HEDE-SS, is presented in order to solve discrete domain optimization problems. This approach is envisioned in order to capture the randomization properties of EDE and the memory adaptation programming (MIAP) properties of SS. Two highly demanding problems of quadratic assignment problem (QAP) and traveling salesman problem (TSP) are optimized with this new heuristic approach. The hybrid obtains the optimal results for almost all of the QAP instances, compares very well for symmetric TSP by getting results around 98 per cent to the optimal. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1001935
utb.identifier.obdid 43865436
utb.identifier.scopus 2-s2.0-58149274885
utb.identifier.wok 000256053700154
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:16Z
dc.date.available 2011-08-09T07:34:16Z
utb.contributor.internalauthor Davendra, Donald David
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