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Multiobjective Optimization

Interactive and Evolutionary Approaches

  • Book
  • © 2008

Overview

Editors:
  1. Jürgen Branke

    1. Institute AIFB, University of Karlsruhe, Germany

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  2. Kalyanmoy Deb

    1. Department of Business Technology, Helsinki School of Economics, Helsinki, Finland

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  3. Kaisa Miettinen

    1. Department of Mathematical Information Technology, University of Jyväskylä, Finland

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  4. Roman Słowiński

    1. Institute of Computing Science Poznan, University of Technology, Poznan, Poland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5252)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

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Table of contents (16 chapters)

  1. Front Matter

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  2. Basics on Multiobjective Optimization

    1. Introduction to Multiobjective Optimization: Noninteractive Approaches

      • Kaisa Miettinen
      Pages 1-26

    2. Introduction to Multiobjective Optimization: Interactive Approaches

      • Kaisa Miettinen, Francisco Ruiz, Andrzej P. Wierzbicki
      Pages 27-57

    3. Introduction to Evolutionary Multiobjective Optimization

      • Kalyanmoy Deb
      Pages 59-96

  3. Recent Interactive and Preference-Based Approaches

    1. Interactive Multiobjective Optimization Using a Set of Additive Value Functions

      • José Rui Figueira, Salvatore Greco, Vincent Mousseau, Roman Słowiński
      Pages 97-119

    2. Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization

      • Salvatore Greco, Benedetto Matarazzo, Roman Słowiński
      Pages 121-155

    3. Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization

      • Jürgen Branke
      Pages 157-178

    4. Interactive Multiobjective Evolutionary Algorithms

      • Andrzej Jaszkiewicz, Jürgen Branke
      Pages 179-193

  4. Visualization of Solutions

    1. Visualization in the Multiple Objective Decision-Making Framework

      • Pekka Korhonen, Jyrki Wallenius
      Pages 195-212

    2. Visualizing the Pareto Frontier

      • Alexander V. Lotov, Kaisa Miettinen
      Pages 213-243

  5. Modelling, Implementation and Applications

    1. Meta-Modeling in Multiobjective Optimization

      • Joshua Knowles, Hirotaka Nakayama
      Pages 245-284

    2. Real-World Applications of Multiobjective Optimization

      • Theodor Stewart, Oliver Bandte, Heinrich Braun, Nirupam Chakraborti, Matthias Ehrgott, Mathias Göbelt et al.
      Pages 285-327

    3. Multiobjective Optimization Software

      • Silvia Poles, Mariana Vassileva, Daisuke Sasaki
      Pages 329-348

    4. Parallel Approaches for Multiobjective Optimization

      • El-Ghazali Talbi, Sanaz Mostaghim, Tatsuya Okabe, Hisao Ishibuchi, Günter Rudolph, Carlos A. Coello Coello
      Pages 349-372

  6. Quality Assessment, Learning, and Future Challenges

    1. Quality Assessment of Pareto Set Approximations

      • Eckart Zitzler, Joshua Knowles, Lothar Thiele
      Pages 373-404

    2. Interactive Multiobjective Optimization from a Learning Perspective

      • Valerie Belton, Jürgen Branke, Petri Eskelinen, Salvatore Greco, Julián Molina, Francisco Ruiz et al.
      Pages 405-433

    3. Future Challenges

      • Kaisa Miettinen, Kalyanmoy Deb, Johannes Jahn, Wlodzimierz Ogryczak, Koji Shimoyama, Rudolf Vetschera
      Pages 435-461

  7. Back Matter

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Keywords

About this book

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.

Editors and Affiliations

  • Institute AIFB, University of Karlsruhe, Germany

    Jürgen Branke

  • Department of Business Technology, Helsinki School of Economics, Helsinki, Finland

    Kalyanmoy Deb

  • Department of Mathematical Information Technology, University of Jyväskylä, Finland

    Kaisa Miettinen

  • Institute of Computing Science Poznan, University of Technology, Poznan, Poland

    Roman Słowiński

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