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Constrained optimization based on a multiobjective evolutionary algorithm
Linköping University.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
2003 (English)In: The 2003 congress on evolutionary computation: CEC 2003, Piscataway, NJ: IEEE Communications Society, 2003, 1560-1567 p.Conference paper (Refereed)
Abstract [en]

A criticism of Evolutionary Algorithms (EAs) might be the lack of efficient and robust generic might be the lack of officient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods. EAs have received increased interest during the last decade due to the ease of handling multiple objectives., A constrained Optimization problem or an unconstrained multiobjective problem may in principle be two different ways to pose the same underlying I problem. In this paper an alternative approach for the constrained optimization problem is presented. The method is a variant of a multiobjective real coded Genetic Algorithm (CA) inspired by the penalty approach. It is evaluated on six different constrained single objective problems found in the literature. The results show that the proposed method performs well in terms of efficiency, and that it is rohust for a majority of the test problems.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2003. 1560-1567 p.
Research subject
Solid Mechanics; Computer Aided Design
URN: urn:nbn:se:ltu:diva-31701DOI: 10.1109/CEC.2003.1299858Local ID: 5f559010-0238-11dd-9241-000ea68e967bISBN: 0-7803-7804-0OAI: diva2:1004935
Congress on evolutionary computation : 08/12/2003 - 12/12/2003
Godkänd; 2003; 20080404 (kirhon)Available from: 2016-09-30 Created: 2016-09-30Bibliographically approved

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Angantyr, AndersAidanpää, Jan-Olov
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