Ebook: Multiobjective Optimization: Interactive and Evolutionary Approaches

Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support.

This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA).

This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.

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Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support.

This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA).

This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.

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Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support.

This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA).

This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.

Content:
Front Matter....Pages -
Introduction to Multiobjective Optimization: Noninteractive Approaches....Pages 1-26
Introduction to Multiobjective Optimization: Interactive Approaches....Pages 27-57
Introduction to Evolutionary Multiobjective Optimization....Pages 59-96
Interactive Multiobjective Optimization Using a Set of Additive Value Functions....Pages 97-119
Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization....Pages 121-155
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization ....Pages 157-178
Interactive Multiobjective Evolutionary Algorithms....Pages 179-193
Visualization in the Multiple Objective Decision-Making Framework....Pages 195-212
Visualizing the Pareto Frontier....Pages 213-243
Meta-Modeling in Multiobjective Optimization....Pages 245-284
Real-World Applications of Multiobjective Optimization....Pages 285-327
Multiobjective Optimization Software....Pages 329-348
Parallel Approaches for Multiobjective Optimization ....Pages 349-372
Quality Assessment of Pareto Set Approximations....Pages 373-404
Interactive Multiobjective Optimization from a Learning Perspective....Pages 405-433
Future Challenges....Pages 435-461
Back Matter....Pages -

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Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support.

This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA).

This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.

Content:
Front Matter....Pages -
Introduction to Multiobjective Optimization: Noninteractive Approaches....Pages 1-26
Introduction to Multiobjective Optimization: Interactive Approaches....Pages 27-57
Introduction to Evolutionary Multiobjective Optimization....Pages 59-96
Interactive Multiobjective Optimization Using a Set of Additive Value Functions....Pages 97-119
Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization....Pages 121-155
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization ....Pages 157-178
Interactive Multiobjective Evolutionary Algorithms....Pages 179-193
Visualization in the Multiple Objective Decision-Making Framework....Pages 195-212
Visualizing the Pareto Frontier....Pages 213-243
Meta-Modeling in Multiobjective Optimization....Pages 245-284
Real-World Applications of Multiobjective Optimization....Pages 285-327
Multiobjective Optimization Software....Pages 329-348
Parallel Approaches for Multiobjective Optimization ....Pages 349-372
Quality Assessment of Pareto Set Approximations....Pages 373-404
Interactive Multiobjective Optimization from a Learning Perspective....Pages 405-433
Future Challenges....Pages 435-461
Back Matter....Pages -
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