Efficiency and robustness of three metaheuristics in the framework of structural optimization

Lagaros, ND; Charmpis, DC

dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Charmpis, DC	en
dc.date.accessioned	2014-03-01T02:46:46Z
dc.date.available	2014-03-01T02:46:46Z
dc.date.issued	2010	en
dc.identifier.issn	18684238	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32837
dc.subject	Differential Evolution	en
dc.subject	Harmony Search	en
dc.subject	Metaheuristics	en
dc.subject	Particle Swarm Optimization	en
dc.subject	Structural Optimization	en
dc.subject.other	Computing demands	en
dc.subject.other	Differential Evolution	en
dc.subject.other	Hardware and software	en
dc.subject.other	Harmony search	en
dc.subject.other	Meta heuristics	en
dc.subject.other	Metaheuristic optimization	en
dc.subject.other	Near-optimal solutions	en
dc.subject.other	Optimizers	en
dc.subject.other	Optimum designs	en
dc.subject.other	Overhead traveling crane	en
dc.subject.other	Particle swarm	en
dc.subject.other	Particle swarm optimization method	en
dc.subject.other	Real-world	en
dc.subject.other	Technological advances	en
dc.subject.other	Test runs	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Biology	en
dc.subject.other	Computer hardware	en
dc.subject.other	Cranes	en
dc.subject.other	Equipment testing	en
dc.subject.other	Heuristic algorithms	en
dc.subject.other	Particle swarm optimization (PSO)	en
dc.subject.other	Shape optimization	en
dc.subject.other	Structural design	en
dc.subject.other	Structural optimization	en
dc.title	Efficiency and robustness of three metaheuristics in the framework of structural optimization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-16239-8_16	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-16239-8_16	en
heal.publicationDate	2010	en
heal.abstract	Due to the technological advances in computer hardware and software tools, structural optimization has been gaining continuously increasing interest over the last two decades. The purpose of the present work is to quantitatively compare three metaheuristic optimization algorithms, namely the Differential Evolution, Harmony Search and Particle Swarm Optimization methods, in the framework of structural optimization. The comparison of the optimizers is performed with reference to their efficiency (overall computing demands) and robustness (capability to detect near-optimal solutions). The optimum design of a real-world overhead traveling crane is used as the test bed application for conducting optimization test runs. © 2010 IFIP.	en
heal.journalName	IFIP Advances in Information and Communication Technology	en
dc.identifier.doi	10.1007/978-3-642-16239-8_16	en
dc.identifier.volume	339 AICT	en
dc.identifier.spage	104	en
dc.identifier.epage	111	en