Sequential Approximate Multiobjective Optimization Using Computational Intelligence (eBook)

Hirotaka Nakayama, Yeboon Yun, Min Yoon (Autoren)

eBook Download: PDF

2009
XVI, 200 Seiten
Springer Berlin (Verlag)
978-3-540-88910-6 (ISBN)

Infos

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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 12.6.2009
• Reihe/Serie: Vector Optimization
• Reihe/Serie: Vector Optimization
• Zusatzinfo: XVI, 200 p. 111 illus.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation
• Schlagworte: algorithms • Evolutionary Multi-objective Optimization • Genetic algorithms • machine learning • meta-modelling • Modeling • Multi-Objective Optimization • Optimal Design of Experiments • Optimization
• ISBN-10: 3-540-88910-8 / 3540889108
• ISBN-13: 978-3-540-88910-6 / 9783540889106