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Advanced solution methods in structural optimization based on evolution strategies

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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


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