A Hybrid Particle Swarm-Gradient Algorithm for Global Structural Optimization

Plevris, V; Papadrakakis, M

dc.contributor.author	Plevris, V	en
dc.contributor.author	Papadrakakis, M	en
dc.date.accessioned	2014-03-01T01:34:53Z
dc.date.available	2014-03-01T01:34:53Z
dc.date.issued	2011	en
dc.identifier.issn	1093-9687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20923
dc.subject	Particle Swarm	en
dc.subject	Structure Optimization	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Basic concepts	en
dc.subject.other	Best estimates	en
dc.subject.other	Bounded rationality	en
dc.subject.other	Constraint-handling techniques	en
dc.subject.other	Decentralized decision making	en
dc.subject.other	Design spaces	en
dc.subject.other	Fast convergence	en
dc.subject.other	Global optimization problems	en
dc.subject.other	Global optimum	en
dc.subject.other	Gradient algorithm	en
dc.subject.other	Gradient based	en
dc.subject.other	Gradient informations	en
dc.subject.other	Hybrid algorithms	en
dc.subject.other	Hybrid particles	en
dc.subject.other	Nonconvex	en
dc.subject.other	Numerical results	en
dc.subject.other	Optimal solutions	en
dc.subject.other	Optimization algorithms	en
dc.subject.other	Optimizers	en
dc.subject.other	Optimum structural design	en
dc.subject.other	PSO algorithms	en
dc.subject.other	Quasi-Newton	en
dc.subject.other	Sequential quadratic programming method	en
dc.subject.other	Setting parameters	en
dc.subject.other	Structural optimization problems	en
dc.subject.other	Weight update	en
dc.subject.other	Algorithms	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Decision making	en
dc.subject.other	Global optimization	en
dc.subject.other	Gradient methods	en
dc.subject.other	Quadratic programming	en
dc.subject.other	Shape optimization	en
dc.subject.other	Structural design	en
dc.subject.other	Structural optimization	en
dc.subject.other	Particle swarm optimization (PSO)	en
dc.subject.other	algorithm	en
dc.subject.other	benchmarking	en
dc.subject.other	computer aided design	en
dc.subject.other	decision analysis	en
dc.subject.other	gradient analysis	en
dc.subject.other	optimization	en
dc.subject.other	parameterization	en
dc.subject.other	performance assessment	en
dc.subject.other	structural analysis	en
dc.title	A Hybrid Particle Swarm-Gradient Algorithm for Global Structural Optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1467-8667.2010.00664.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1467-8667.2010.00664.x	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO-based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient-based quasi-Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms. © 2010 Computer-Aided Civil and Infrastructure Engineering.	en
heal.publisher	WILEY-BLACKWELL PUBLISHING, INC	en
heal.journalName	Computer-Aided Civil and Infrastructure Engineering	en
dc.identifier.doi	10.1111/j.1467-8667.2010.00664.x	en
dc.identifier.isi	ISI:000285760600004	en
dc.identifier.volume	26	en
dc.identifier.issue	1	en
dc.identifier.spage	48	en
dc.identifier.epage	68	en