Multiobjective Optimization

Interactive and Evolutionary Approaches

  • Jürgen Branke
  • Kalyanmoy Deb
  • Kaisa Miettinen
  • Roman Słowiński
Conference proceedings

DOI: 10.1007/978-3-540-88908-3

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

Table of contents (16 papers)

  1. Front Matter
  2. Basics on Multiobjective Optimization

  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. 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

About these proceedings

Introduction

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.

Keywords

GUI design algorithms combinatorial optimization constraint satisfaction problems decision maps evolutionary algorithm evolutionary algorithms evolutionary computation fuzzy rule genetic algorithms heuristics hybrid methods information retrieval interactive methods knapsack pr

Editors and affiliations

  • Jürgen Branke
    • 1
  • Kalyanmoy Deb
    • 2
  • Kaisa Miettinen
    • 3
  • Roman Słowiński
    • 4
  1. 1.Institute AIFBUniversity of KarlsruheGermany
  2. 2.Department of Business TechnologyHelsinki School of EconomicsHelsinkiFinland
  3. 3.Department of Mathematical Information TechnologyUniversity of JyväskyläFinland
  4. 4.Institute of Computing Science PoznanUniversity of TechnologyPoznanPoland

Bibliographic information

  • Copyright Information Springer Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-88907-6
  • Online ISBN 978-3-540-88908-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349