Mixed-variable engineering optimization based on evolutionary and social metaphors

Dimopoulos, GG

dc.contributor.author	Dimopoulos, GG	en
dc.date.accessioned	2014-03-01T01:26:41Z
dc.date.available	2014-03-01T01:26:41Z
dc.date.issued	2007	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18165
dc.subject	Evolutionary algorithms	en
dc.subject	Hybrid algorithms	en
dc.subject	Mixed-variable optimization	en
dc.subject	Particle swarm optimization	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Function evaluation	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Problem solving	en
dc.subject.other	Mixed-variable optimization	en
dc.subject.other	Particle swarm optimization	en
dc.subject.other	Simple genetic algorithm	en
dc.subject.other	Struggle genetic algorithm	en
dc.subject.other	Optimization	en
dc.subject.other	Function evaluation	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.title	Mixed-variable engineering optimization based on evolutionary and social metaphors	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cma.2006.06.010	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cma.2006.06.010	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	The co-existence of discrete and continuous independent variables in an engineering optimization problem with a multimodal objective function makes many methods incapable of solving the problem. Four methods are tested here: (a) a Simple Genetic Algorithm (SGA), (b) a Struggle Genetic Algorithm (StrGA), (c) a Particle Swarm Optimization Algorithm (PSOA), and (d) a Particle Swarm Optimization Algorithm with Struggle Selection (PSOStr). The last one has been developed by the author, and it is a hybrid of the evolutionary StrGA and the socially inspired PSOA. They are tested in four purely mathematical and three engineering optimization problems of the aforementioned type. All of the methods solved successfully all the problems and located the global optimum. The PSOStr, however, outperformed the other methods in terms of both solution accuracy and computational cost (i.e. function evaluations). (c) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/j.cma.2006.06.010	en
dc.identifier.isi	ISI:000242648300006	en
dc.identifier.volume	196	en
dc.identifier.issue	4-6	en
dc.identifier.spage	803	en
dc.identifier.epage	817	en