Advanced solution methods in structural optimization based on evolution strategies

Papadrakakis, M; Lagaros, ND; Thierauf, G; Cai, J

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Thierauf, G	en
dc.contributor.author	Cai, J	en
dc.date.accessioned	2014-03-01T01:13:33Z
dc.date.available	2014-03-01T01:13:33Z
dc.date.issued	1998	en
dc.identifier.issn	0264-4401	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12562
dc.subject	evolution strategies	en
dc.subject	hybrid techniques	en
dc.subject	structural optimization	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Computational methods	en
dc.subject.other	Finite element method	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Iterative methods	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Matrix algebra	en
dc.subject.other	Neural networks	en
dc.subject.other	Parallel processing systems	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Problem solving	en
dc.subject.other	Simulated annealing	en
dc.subject.other	Structural optimization	en
dc.subject.other	Algorithms	en
dc.subject.other	Calculations	en
dc.subject.other	Mathematical techniques	en
dc.subject.other	Optimization	en
dc.subject.other	Performance	en
dc.subject.other	Evolution strategies	en
dc.subject.other	Evolution-based systems	en
dc.subject.other	Natural processes	en
dc.subject.other	Selection process	en
dc.subject.other	Hybrid techniques	en
dc.subject.other	Strategic planning	en
dc.subject.other	Structural design	en
dc.title	Advanced solution methods in structural optimization based on evolution strategies	en
heal.type	journalArticle	en
heal.identifier.primary	10.1108/02644409810200668	en
heal.identifier.secondary	http://dx.doi.org/10.1108/02644409810200668	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	The objective of this paper is to investigate the efficiency of hybrid solution methods when incorporated into large-scale optimization problems solved by evolution strategies (ESs) and to demonstrate their influence on the overall performance of these optimization algorithms. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this paper modified multi-membered evolution strategies with discrete variables are adopted. Two solution methods are implemented based on the preconditioned conjugate gradient (PCG) algorithm. The first method is a PCG algorithm with a preconditioner resulted from a complete Cholesky factorization, and the second is a PCG algorithm in which a truncated Neumann series expansion is used as a preconditioner. The numerical tests presented demonstrate the computational advantages of the proposed methods, which become more pronounced in large-scale optimization problems and in a parallel computing environment.	en
heal.publisher	MCB UNIV PRESS LTD	en
heal.journalName	Engineering Computations (Swansea, Wales)	en
dc.identifier.doi	10.1108/02644409810200668	en
dc.identifier.isi	ISI:000072522700002	en
dc.identifier.volume	15	en
dc.identifier.issue	1	en
dc.identifier.spage	12	en
dc.identifier.epage	34	en