Study on multiobjective optimization using improved genetic Algorithm: Methodology and Application - Couverture souple

Mousa, Abd Allah A.

9783846548899: Study on multiobjective optimization using improved genetic Algorithm: Methodology and Application

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ISBN 10 : 3846548898 ISBN 13 : 9783846548899

Editeur : LAP LAMBERT Academic Publishing, 2011

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Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems.

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A. A. Mosua obtained his PhD in in engineering mathematics from faculty of engineering, Menoufia University, Egypt. He is currently a Lecturer in the Basic Engineering Sciences, Faculty of Engineering, Menoufia University. His research interests are evolutionary multiobjective optimization, mathematical programming, artificial intelligence methods.

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�diteurLAP LAMBERT Academic Publishing
Date d'�dition2011
ISBN 10 3846548898
ISBN 13 9783846548899
ReliureBroch�
Langueanglais
Nombre de pages144
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Study on multiobjective optimization using improved genetic Algorithm

Abd Allah A. Mousa

Edit� par LAP LAMBERT Academic Publishing, 2011

ISBN 10 : 3846548898 ISBN 13 : 9783846548899

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mousa Abd Allah A.A. A. Mosua obtained his PhD in in engineering mathematics from faculty of engineering, Menoufia University, Egypt. He is currently a Lecturer in the Basic Engineering Sciences, Faculty of Engineering, Menoufia Univ. N� de r�f. du vendeur 5498323

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Study on multiobjective optimization using improved genetic Algorithm : Methodology and Application

Abd Allah A. Mousa

Edit� par LAP LAMBERT Academic Publishing, 2011

ISBN 10 : 3846548898 ISBN 13 : 9783846548899

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems. N� de r�f. du vendeur 9783846548899

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Study on multiobjective optimization using improved genetic Algorithm

Abd Allah A. Mousa

Edit� par LAP LAMBERT Academic Publishing Nov 2011, 2011

ISBN 10 : 3846548898 ISBN 13 : 9783846548899

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems. 144 pp. Englisch. N� de r�f. du vendeur 9783846548899

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Study on multiobjective optimization using improved genetic Algorithm

Abd Allah A. Mousa

Edit� par LAP LAMBERT Academic Publishing Nov 2011, 2011

ISBN 10 : 3846548898 ISBN 13 : 9783846548899

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Taschenbuch. Etat : Neu. Neuware -Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems.Books on Demand GmbH, �berseering 33, 22297 Hamburg 144 pp. Englisch. N� de r�f. du vendeur 9783846548899

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