極值優化：原理、算法和應用（英文版）

Preface
Acknowledgments
SECTION Ⅰ FUNDAMENTALS， METHODOLOGY， AND ALGORITHMS
1 General Introduction
1.1 Introduction
1.2 Understanding Optimization：From Practical Aspects
1.2.1 Mathematical Optimization
1.2.2 Optimization： From Practical Aspects
1.2.3 Example Applications of Optimization
1.2.4 Problem Solving for Optimization
1.3 Phase Transition and Computational Complexity
1.3.1 Computational Complexity in General
1.3.2 Phase Transitionin Computation
1.4 CI—Inspired Optimization
1.4.1 Evolutionary Computations
1.4.2 Swarm Intelligence
1.4.3 Data Mining and Machine Learning
1.4.4 Statistical Physics
1.5 Highlights of EO
1.5.1 Self—Organized Criticality and EO
1.5.2 Coevolution， Ecosystems， and Bak—Sneppen Model
1.5.3 Comparing EO with SA and GA
1.5.4 Challenging Open Problems
1.6 Organization of the Book
2 Introduction to Extremal Optimization
2.1 Optimization with Extremal Dynamics
2.2 Multidisciplinary Analysis of EO
2.3 Experimental and Comparative Analysis on the Traveling Salesman Problems
2.3.1 EO for the Symmetric TSP
2.3.1.1 Problem Formulation and Algorithm Design
2.3.2 SA versus Extremal Dynamics
2.3.3 Optimizing Near the Phase Transition
2.3.4 EO for the Asymmetric TSP
2.3.4.1 Cooperative Optimization
2.3.4.2 Parameter Analysis
2.4 Summary
3 Extremal Dynamics—Inspired Self—Organizing Optimization
3.1 Introduction
3.2 Analytic Characterization of COPs
3.2.1 Modeling COPs into Multientity Systems
3.2.2 Local Fitness Function
3.2.3 Microscopic Analysis of Optimal Solutions
3.2.4 Neighborhood and Fitness Network
3.2.5 Computational Complexity and Phase Transition
3.3 Self—Organized Optimization
3.3.1 Self—Organized Optimization Algorithm
3.3.2 Comparison with Related Methods
3.3.2.1 Simulated Annealing
3.3.2.2 Genetic Algorithm
3.3.2.3 Extremal Optimization
3.3.3 Experimental Validation
3.4 Summary
SECTION Ⅱ MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE
4 Modified Extremal Optimization
4.1 Introduction
4.2 Modified EO with Extended Evolurionary Probability Distribution
4.2.1 Evolutionary Probability Distribution
4.2.2 Modified EO Algorithm with Extended Evolutionary Probability Distribution
4.2.3 Experimental Results
4.3 Multistage EO
4.3.1 Motivations
4.3.2 MSEO Algorithm
4.3.3 Experimental Results
4.3.3.1 The Simplest Case： Two—Stage EO
4.3.3.2 Complex Case
4.3.4 Adjustable Parameters versus Performance
4.4 Backbone—Guided EO
4.4.1 Definitions of Fitness and Backbones
4.4.2 BGEO Algorithm
4.4.3 Experimental Results
4.5 Population—Based EO
4.5.1 Problem Formulation of Numerical Constrained Optimization Problems
4.5.2 PEO Algorithm
4.5.3 Mutation Operator
4.5.4 Experimental Results
4.5.5 Advantages of PEO
4.6 Summary
5 Memetic Algorithms with Extremal Optimization
5.1 Introduction to MAs
5.2 Design Principle of MAs
5.3 EO—LM Integration
5.3.1 Introduction
5.3.2 Problem Statement and Math Formulation
5.3.3 Introduction of LM GS
5.3.4 MA—Based Hybrid EO—LM Algorithm
5.3.5 Fitness Function
5.3.6 Experimental Tests on Benchmark Problems
5.3.6.1 A Multi—Input， Single—Output Static Nonlinear Function
5.3.6.2 Five—Dimensional Ackley Function Regression
5.3.6.3 Dynamic Modeling for Continuously Stirred Tank Reactor
5.4 EO—SQP Integration
5.4.1 Introduction
5.4.2 Problem Formularion
5.4，3 Introduction of SQP
5.4.4 MA—Based Hybrid EO—SQP Algorithm
5.4，5 Fitness Function Definition
5.4.6 Termination Criteria
5.4.7 Workflow and Algorithm
5.4.8 Experimental Tests on Benchmark Functions
5.4.8.1 Unconstrained Problems
5.4.8.2 Constrained Problems
5.4.9 Dynamics Analysis of the Hybrid EO—SQP
5.5 EO—PSO Integration
5.5.1 Introduction
5.5.2 Particle Swarm Optimization
5.5.3 PSO—EO Algorithm
5.5.4 Mutation Operator
5.5.5 Computational Complexity
5.5.6 Experimental Results
5.6 EO—ABC Integration
5.6.1 Artificial Bee Colony
5.6.2 ABC—EO Algorithm
5.6.3 Mutation Operator
5.6.4 DifFerences between ABC—EO and Other Hybrid Algorithms
5.6.5 Experimental Results
5.7 EO—GA Integration
5.8 Summary
6 Multiobjective Optimization with Extremal Dynamics
6.1 Introduction
6.2 Problem Statement and Definition
6.3 Solutions to Multiobjective Optimization
6.3.1 Aggregating Functions
6.3.2 Population—Based Non—Pareto Approaches
6.3.3 Pareto—Based Approaches
6.4 EO for Numerical MOPs
6.4.1 MOEO Algorithm
6.4.1.1 Fitness Assignment
6.4.1.2 Diversity Preservation
6.4.1.3 External Archive
6.4.1.4 Mutation Operation
6.4.2 Unconstrained Numerical MOPs with MOEO
6.4.2.1 Performance Metrics
6.4.2.2 Experimental Settings
6.4.2.3 Experimental Results and Discussion
6，4.2.4 Conclusions
6.4.3 Constrained Numerical MOPs with MOEO
6.4.3.1 Performance Metrics
6.4.3.2 Experimental Settings
6.4.3.3 Experimental Results and Discussion
6.4.3.4 Conclusions
6.5 Multiobjective 0／1 Knapsack Problem with MOEO
6.5.1 Extended MOEO for MOKP
6.5.1.1 Mutation Operation
6.5.1.2 Repair Strategy
6.5.2 Experimental Settings
6.5.3 Experimental Results and Discussion
6.5.4 Conclusions
6.6 Mechanical Components Design with MOEO
6.6.1 Introduction
6.6.2 Experimental Settings
6.6.2.1 Two—Bar Truss Design （Two Bar for Short）
6.6.2.2 Welded Beam Design （Welded Beam for Short）
6.6.2.3 Machine Tool Spindle Design （Spindle for Short）
6.6.3 Experimental Results and Discussion
6.6.4 Conclusions
6.7 Portfolio Optimization with MOEO
6.7.1 Portfolio Optimization Model
6.7.2 MOEO for Portfolio Optimization Problems
6.7.2.1 Mutation Operation
6.7.2.2 Repair Strategy
6.7.3 Experimental Settings
6.7.4 Experimental Results and Discussion
6.7.5 Conclusions
6.8 Summary
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SECTION Ⅲ APPLICATIONS
References
Author Index
Subject Index