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Introduction to Evolutionary Multiobjective Optimization

  • Chapter
Multiobjective Optimization

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

Abstract

In its current state, evolutionary multiobjective optimization (EMO) is an established field of research and application with more than 150 PhD theses, more than ten dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this chapter, we provide a brief introduction to EMO principles, illustrate some EMO algorithms with simulated results, and outline the current research and application potential of EMO. For solving multiobjective optimization problems, EMO procedures attempt to find a set of well-distributed Pareto-optimal points, so that an idea of the extent and shape of the Pareto-optimal front can be obtained. Although this task was the early motivation of EMO research, EMO principles are now being found to be useful in various other problem solving tasks, enabling one to treat problems naturally as they are. One of the major current research thrusts is to combine EMO procedures with other multiple criterion decision making (MCDM) () tools so as to develop hybrid and interactive multiobjective optimization algorithms for finding a set of trade-off optimal solutions and then choose a preferred solution for implementation. This chapter provides the background of EMO principles and their potential to launch such collaborative studies with MCDM researchers in the coming years.

Reviewed by: Matthias Ehrgott, The University of Auckland, New Zealand; Christian Igel, Ruhr-Universität Bochum, Germany

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, PIN 208016, India

    Kalyanmoy Deb

  2. Department of Business Technology, Helsinki School of Economics, PO Box 1210, 00101, Helsinki, Finland

    Kalyanmoy Deb

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  1. Kalyanmoy Deb
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Editors and Affiliations

  1. Institute AIFB, University of Karlsruhe, Germany

    Jürgen Branke

  2. Department of Business Technology, Helsinki School of Economics, P.O. Box 1210, FI-00101, Helsinki, Finland

    Kalyanmoy Deb

  3. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, (Agora), FI-40014, Finland

    Kaisa Miettinen

  4. Institute of Computing Science Poznan, University of Technology, 60-965, Poznan, Poland

    Roman Słowiński

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Deb, K. (2008). Introduction to Evolutionary Multiobjective Optimization. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds) Multiobjective Optimization. Lecture Notes in Computer Science, vol 5252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88908-3_3

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