Evolutionary Multiobjective Optimization

Cover -- Preface -- Table of Contents -- List of Contributors -- 1 Evolutionary Multiobjective Optimization -- 1.1 What is Multiobjective Optimization? -- 1.2 Why Use Evolutionary Algorithms for Multiobjective Optimization? -- 1.3 Evolutionary Multiobjective Optimization: Challenges, Advances and Applications -- References -- 2 Recent Trends in Evolutionary Multiobjective Optimization -- 2.1 Introduction -- 2.2 Basic Concepts -- 2.3 Algorithms -- 2.4 Applications -- 2.5 Test Functions -- 2.6 Metrics -- 2.7 Theory -- 2.8 Promising Paths for Future Research -- 2.9 Conclusions -- Acknowledgments -- References -- 3 Self-adaptation and Convergence of Multiobjective Evolutionary Algorithms in Continuous Search Spaces -- 3.1 Introduction -- 3.2 Self-adaptive Mutation Control. -- 3.3 The Model Problem -- 3.4 Non-elitist Strategies ("komma" Selection) -- 3.5 Pseudo-elitist Strategies ("plus" Selection) -- 3.6 Elitist Strategies -- 3.7 Conclusions -- Acknowledgments -- References -- 4 A Simple Approach to Evolutionary Multiobjective Optimization -- 4.1 Introduction -- 4.2 Pareto-based Multiobjective Optimization -- 4.3 An Overview of Evolutionary Techniques for Multi objective Optimization -- 4.4 The 0-1 Multiple Knapsack Problem -- 4.5 An Introduction to SEAMO -- 4.6 Illustrating SEAMO Using a Small Multiple Knapsack Problem -- 4.7 A Summary of Results for Multiple Knapsack Problems -- 4.8 Implementing SEAMO to Solve Continuous Test Problems -- 4.9 Summary of Results for the Continuous Functions -- 4.10 Conclusions -- Acknowledgments -- References -- 5 Quad-trees: A Data Structure for Storing Pareto Sets in Multiobjective Evolutionary Algorithms with Elitism -- 5.1 Introduction -- 5.2 Multiobjective Evolutionary Algorithms (MOEAs) -- 5.3 Data Structures for Storing Non-dominated Solutions in MOEA -- 5.4 Use of Quad-trees in MOEA -- 5.5 Experiments -- 5.6 Conclusion and Future Work -- Acknowledgment -- References -- 6 Scalable Test Problems for Evolutionary Multiobjective Optimization -- 6.1 Introduction -- 6.2 Desired Features of Test Problems -- 6.3 Different Methods of Test Problem Design -- 6.4 Bottom-up Approach -- 6.5 Constraint Surface Approach -- 6.6 Difficulties with Existing MOEAs -- 6.7 Test Problem Suite -- 6.8 Redundant Solutions -- 6.9 Conclusions -- Acknowledgments -- Another Test Problem Using Bottom-up Approach The Comet Problem -- References -- 7 Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems -- 7.1 Introduction -- 7.2 Particles Swarm Optimization. -- 7.3 Particles Swarm Inspired Evolutionary Algorithm (PS-EA -- 7.4 Benchmarking Experiments -- 7.5 Simulation Results and Discussion -- 7.6 Conclusions -- References -- 8 Evolving Continuous Pareto Regions -- 8.1 Introduction -- 8.2 Problem Statement -- 8.3 Solution Representation and Domination -- 8.4 e-dominance -- 8.5 Mutation -- 8.6 Population Model -- 8.7 Algorithm Complexity -- 8.8 PECR Algorithm -- 8.9 Numerical Experiments -- 8.10 Comparison of Several Multiobjective Evolutionary Algorithms --tid.