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A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7811))

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Abstract

Many-objective optimization has been gaining increasing attention in the evolutionary multiobjective optimization community, and various approaches have been developed to solve many-objective problems in recent years. However, the existing empirically comparative studies are often restricted to only a few approaches on a handful of test problems. This paper provides a systematic comparison of eight representative approaches from the six angles to solve many-objective problems. The compared approaches are tested on four groups of well-defined continuous and combinatorial test functions, by three performance metrics as well as a visual observation in the decision space. We conclude that none of the approaches has a clear advantage over the others, although some of them are competitive on most of the problems. In addition, different search abilities of these approaches on the problems with different characteristics suggest a careful choice of approaches for solving a many-objective problem in hand.

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Author information

Authors and Affiliations

  1. Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, United Kingdom

    Miqing Li & Xiaohui Liu

  2. School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  3. Institute of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Ruimin Shen

Authors
  1. Miqing Li
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  2. Shengxiang Yang
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  3. Xiaohui Liu
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  4. Ruimin Shen
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Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Robin C. Purshouse

  2. Department of Automatic Control Systems and Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Peter J. Fleming

  3. Department of Informatics Engineering, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Carlos M. Fonseca

  4. Department of Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

  5. Unilever Research & Development Port Sunlight, Quarry Road East, CH63 3JW, Merseyside, UK

    Jane Shaw

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© 2013 Springer-Verlag Berlin Heidelberg

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Li, M., Yang, S., Liu, X., Shen, R. (2013). A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_22

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  • DOI: https://doi.org/10.1007/978-3-642-37140-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37139-4

  • Online ISBN: 978-3-642-37140-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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