Enhanced differential evolution hybrid scatter search for discrete optimization

Davendra, Donald David; Onwubolu, Godfrey

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