Sequential Approximate Multiobjective Optimization Using Computational Intelligence (eBook)

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