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J. Cai, G. Thierauf, "A Parallel Evolution Strategy for Solving Discrete Structural Opimization", in B.H.V. Topping, M. Papadrakakis, (Editors), "Advances in Parallel and Vector Processing for Structural Mechanics", Civil-Comp Press, Edinburgh, UK, pp 239-244, 1994. doi:10.4203/ccp.20.7.1

Abstract

A method to solve optimization problems with discrete variables by using modified Evolution strategies (ESs) is presented. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism.

An important characteristic of ESs that differs from other conventional optimization algorithms is that in place of a single design point the ESs work simultaneously with a population of design points in the space of variables. This allows for an implementation in a parallel computing environment. In this paper the modified ESs for solving discrete optimization problems and its parallelization are described.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 20 ADVANCES IN PARALLEL AND VECTOR PROCESSING FOR STRUCTURAL MECHANICS Edited by: B.H.V. Topping and M. Papadrakakis Paper VII.1 A Parallel Evolution Strategy for Solving Discrete Structural Opimization J. Cai and G. Thierauf Baumechanik-Baustatik, Fachbereich Bauwesen, Universitat GH Essen, Essen, Germany doi:10.4203/ccp.20.7.1 purchase the full-text of this paper Full Bibliographic Reference for this paper J. Cai, G. Thierauf, "A Parallel Evolution Strategy for Solving Discrete Structural Opimization", in B.H.V. Topping, M. Papadrakakis, (Editors), "Advances in Parallel and Vector Processing for Structural Mechanics", Civil-Comp Press, Edinburgh, UK, pp 239-244, 1994. doi:10.4203/ccp.20.7.1 Abstract A method to solve optimization problems with discrete variables by using modified Evolution strategies (ESs) is presented. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. An important characteristic of ESs that differs from other conventional optimization algorithms is that in place of a single design point the ESs work simultaneously with a population of design points in the space of variables. This allows for an implementation in a parallel computing environment. In this paper the modified ESs for solving discrete optimization problems and its parallelization are described. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £60 +P&P)
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