Nakayama / Yun / Yoon Sequential Approximate Multiobjective Optimization Using Computational Intelligence

1. Auflage 2009
ISBN: 978-3-540-88910-6
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 200 Seiten

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.

Autoren/Hrsg.

Nakayama, Hirotaka

Yun, Yeboon

Yoon, Min

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;List of Tables;11
4;List of Figures;12
5;Chapter 1 Basic Concepts of Multiobjective Optimization;16
5.1;1.1 Mathematical Foundations;16
5.2;1.2 Preference Order and Domination Set;19
5.3;1.3 Scalarization;20
5.4;1.4 Scalarization and Trade-o. Analysis;26
6;Chapter 2 Interactive Programming Methods for Multiobjective Optimization;31
6.1;2.1 Goal Programming;31
6.2;2.2 Why is the Weighting Method Ine.ective?;34
6.3;2.3 Satis.cing Trade-o. Method;36
6.4;2.4 Applications;48
6.5;2.5 Some Remarks on Applications;56
7;Chapter 3 Generation of Pareto Frontier by Genetic Algorithms;58
7.1;3.1 Evolutionary Multiobjective Optimization;58
7.2;3.2 Fitness Evaluation Using DEA;69
7.3;3.3 Fitness Evaluation Using GDEA;77
7.4;3.4 Comparisons of Several Fitness Evaluations;80
8;Chapter 4 Multiobjective Optimization and Computational Intelligence;85
8.1;4.1 Machine Learning;85
8.2;4.2 Radial Basis Function Networks;91
8.3;4.3 Support Vector Machines for Pattern Classi.cation;95
8.4;4.4 Support Vector Machines for Regression;110
8.5;4.5 Combining Predetermined Model and SVR/RBFN;122
9;Chapter 5 Sequential Approximate Optimization;125
9.1;5.1 Metamodels;125
9.2;5.2 Optimal Design of Experiments;127
9.3;5.3 Distance-Based Criteria for Optimal Design;132
9.4;5.4 Incremental Design of Experiments;134
9.5;5.5 Kriging and E.cient Global Optimization;138
9.6;5.6 Distance-Based Local and Global Information;153
10;Chapter 6 Combining Aspiration Level Approach and SAMO;162
10.1;6.1 Sequential Approximate Multiobjective Optimization Using Satis.cing Trade-o. Method;163
10.2;6.2 MCDM with Aspiration Level Method and GDEA;170
10.3;6.3 Discussions;178
11;Chapter 7 Engineering Applications;180
11.1;7.1 Reinforcement of Cable-Stayed Bridges;180
11.2;7.2 Multiobjective Startup Scheduling of Power Plants;187
11.3;References;195
12;Index;203