Sequential Approximate Multiobjective Optimization Using Computational Intelligence (Vector Optimization)

Nakayama, Hirotaka; Yun, Yeboon; Yoon, Min

ISBN 10: 3642100309 / ISBN 13: 9783642100307

Verlag: Springer, 2011

Beschreibung

Bestandsnummer des Verkäufers I-9783642100307

Bibliografische Details

• Titel: Sequential Approximate Multiobjective Optimization Using Computational Intelligence (Vector Optimization)
• Verlag: Springer
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 3642100309
• ISBN-13: 9783642100307
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Many kinds of practical problems such as engineering design, industrial m- agement and ?nancial investment have multiple objectives con?icting with eachother. Thoseproblemscanbeformulatedasmultiobjectiveoptimization. In multiobjective optimization, there does not necessarily a unique solution which minimizes (or maximizes) all objective functions. We usually face to the situation in which if we want to improve some of objectives, we have to give up other objectives. Finally, we pay much attention on how much to improve some of objectives and instead how much to give up others. This is called “trade-o?. ” Note that making trade-o? is a problem of value ju- ment of decision makers. One of main themes of multiobjective optimization is how to incorporate value judgment of decision makers into decision s- port systems. There are two major issues in value judgment (1) multiplicity of value judgment and (2) dynamics of value judgment. The multiplicity of value judgment is treated as trade-o? analysis in multiobjective optimi- tion. On the other hand, dynamics of value judgment is di?cult to treat. However, it is natural that decision makers change their value judgment even in decision making process, because they obtain new information during the process. Therefore, decision support systems are to be robust against the change of value judgment of decision makers. To this aim, interactive p- grammingmethodswhichsearchasolutionwhileelicitingpartialinformation on value judgment of decision makers have been developed. Those methods are required to perform ?exibly for decision makers’ attitude.

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This book highlights a new direction of multiobjective optimzation, which has never been treated in previous publications. When the function form of objective functions is not known explicitly as encountered in many practical problems, sequential approximate optimization based on metamodels is an effective tool from a practical viewpoint. Several sophisticated methods for sequential approximate multiobjective optimization using computational intelligence are introduced along with real applications, mainly engineering problems, in this book.