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M. Papadrakakis, N.D. Lagaros and G. Kokassalakis

Institute of Structural Analysis and Seismic Research, National Technical University of Athens, Greece

doi:10.4203/csets.4.13

M. Papadrakakis, N.D. Lagaros, G. Kokassalakis, "Evolutionary Algorithms applied to Structural Optimization Problems", in B.H.V. Topping, L. Lämmer, (Editors), "High Performance Computing for Computational Mechanics", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 13, pp 207-233, 2000. doi:10.4203/csets.4.13

Abstract

The objective of this study is to investigate the efficiency of various Evolutionary Algorithms (EAs), such as Evolution Strategies (ESs) and Genetic Algorithms (GAs), when applied to large-scale sizing optimization problems. ESs and GAs imitate biological evolution in nature and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism The proposed methods are compared with a conventional mathematical programming (MP) method. A hybrid methodology. namely GAs-MP is also proposed in order to combine the advantages of both methods. The numerical tests presented demonstrate the computational advantages of the proposed methods which become more pronounced in large-scale optimization problems.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 4 HIGH PERFORMANCE COMPUTING FOR COMPUTATIONAL MECHANICS Edited by: B.H.V. Topping, L. Lämmer Chapter 13 Evolutionary Algorithms applied to Structural Optimization Problems M. Papadrakakis, N.D. Lagaros and G. Kokassalakis Institute of Structural Analysis and Seismic Research, National Technical University of Athens, Greece doi:10.4203/csets.4.13 purchase the full-text of this chapter Full Bibliographic Reference for this chapter M. Papadrakakis, N.D. Lagaros, G. Kokassalakis, "Evolutionary Algorithms applied to Structural Optimization Problems", in B.H.V. Topping, L. Lämmer, (Editors), "High Performance Computing for Computational Mechanics", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 13, pp 207-233, 2000. doi:10.4203/csets.4.13 Abstract The objective of this study is to investigate the efficiency of various Evolutionary Algorithms (EAs), such as Evolution Strategies (ESs) and Genetic Algorithms (GAs), when applied to large-scale sizing optimization problems. ESs and GAs imitate biological evolution in nature and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism The proposed methods are compared with a conventional mathematical programming (MP) method. A hybrid methodology. namely GAs-MP is also proposed in order to combine the advantages of both methods. The numerical tests presented demonstrate the computational advantages of the proposed methods which become more pronounced in large-scale optimization problems. purchase the full-text of this chapter (price £20) go to the previous chapter return to the table of contents return to the book description purchase this book (price £75 +P&P)
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