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Convergence Versus Diversity in Multiobjective Optimization

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Parallel Problem Solving from Nature – PPSN XIV (PPSN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9921))

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Abstract

Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting based selection method. While convergence-first sorting has continuously shown effectiveness for handling a variety of problems, it faces challenges to maintain well population diversity due to the overemphasis of convergence. In this paper, we propose a general diversity-first sorting method for multiobjective optimization. Based on the method, a new MOEA, called DBEA, is then introduced. DBEA is compared with the recently-developed nondominated sorting genetic algorithm III (NSGA-III) on different problems. Experimental studies show that the diversity-first method has great potential for diversity maintenance and is very competitive for many-objective optimization.

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Notes

  1. 1.

    Note that, although some algorithms like SPEA2 sort individuals by exploiting both convergence and diversity, convergence is priorly considered and emphasised.

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Acknowledgments

This work was funded by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1.

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Authors and Affiliations

  1. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Shouyong Jiang & Shengxiang Yang

Authors
  1. Shouyong Jiang
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  2. Shengxiang Yang
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Corresponding author

Correspondence to Shouyong Jiang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Julia Handl

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Emma Hart

  3. Aston University, Birmingham, United Kingdom

    Peter R. Lewis

  4. University of Manchester, Manchester, United Kingdom

    Manuel López-Ibáñez

  5. University of Stirling, Stirling, United Kingdom

    Gabriela Ochoa

  6. Edinburgh Napier University, Edinburgh, United Kingdom

    Ben Paechter

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© 2016 Springer International Publishing AG

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Jiang, S., Yang, S. (2016). Convergence Versus Diversity in Multiobjective Optimization. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_92

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  • DOI: https://doi.org/10.1007/978-3-319-45823-6_92

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45822-9

  • Online ISBN: 978-3-319-45823-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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