Large Scale Structural Optimization: Computational Methods and Optimization Algorithms

Papadrakakis, M; Lagaros, ND; Tsompanakis, Y; Plevris, V

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Tsompanakis, Y	en
dc.contributor.author	Plevris, V	en
dc.date.accessioned	2014-03-01T01:16:40Z
dc.date.available	2014-03-01T01:16:40Z
dc.date.issued	2001	en
dc.identifier.issn	1134-3060	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14161
dc.subject	Computational Method	en
dc.subject	Domain Decomposition	en
dc.subject	Dynamic Analysis	en
dc.subject	Evolution Strategy	en
dc.subject	Evolutionary Algorithm	en
dc.subject	Genetic Algorithm	en
dc.subject	Large Scale	en
dc.subject	Large Scale Optimization	en
dc.subject	Large Scale Structure	en
dc.subject	Mathematical Programming	en
dc.subject	Optimal Algorithm	en
dc.subject	Optimal Design	en
dc.subject	Optimal Method	en
dc.subject	Optimum Design	en
dc.subject	Sensitivity Analysis	en
dc.subject	Spectrum	en
dc.subject	Structure Analysis	en
dc.subject	Structure Optimization	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.other	NEURAL NETWORKS	en
dc.subject.other	EVOLUTION STRATEGIES	en
dc.subject.other	OPTIMAL-DESIGN	en
dc.subject.other	IMPLEMENTATION	en
dc.subject.other	DYNAMICS	en
dc.title	Large Scale Structural Optimization: Computational Methods and Optimization Algorithms	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/BF02736645	en
heal.identifier.secondary	http://dx.doi.org/10.1007/BF02736645	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	The objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programming and evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particular emphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimization procedure. Modified versions of both genetic algorithms and evolution strategies combined with mathematical programming methods to form hybrid methodologies are also tested and compared and proved particularly promising. Furthermore, the structural analysis phase is replaced by a neural network prediction for the computation of the necessary data required by the evolutionary algorithms. Advanced domain decomposition techniques particularly tailored for parallel solution of large-scale sensitivity analysis problems are also implemented. The efficiency of a rigorous approach for treating seismic loading is investigated and compared with a simplified dynamic analysis adopted by seismic codes in the framework of finding the optimum design of structures with minimum weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. The numerical tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale optimization problems.	en
heal.publisher	INT CENTER NUMERICAL METHODS ENGINEERING (CIMNE)	en
heal.journalName	Archives of Computational Methods in Engineering	en
dc.identifier.doi	10.1007/BF02736645	en
dc.identifier.isi	ISI:000172623100001	en
dc.identifier.volume	8	en
dc.identifier.issue	3	en
dc.identifier.spage	239	en
dc.identifier.epage	301	en