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SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2

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Parallel Problem Solving from Nature - PPSN VIII (PPSN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3242))

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Abstract

Multi-objective optimization methods are essential to resolve real-world problems as most involve several types of objects. Several multi-objective genetic algorithms have been proposed. Among them, SPEA2 and NSGA-II are the most successful. In the present study, two new mechanisms were added to SPEA2 to improve its searching ability a more effective crossover mechanism and an archive mechanism to maintain diversity of the solutions in the objective and variable spaces. The new SPEA2 with these two mechanisms was named SPEA2+. To clarify the characteristics and effectiveness of the proposed method, SPEA2+ was applied to several test functions. In the comparison of SPEA2+ with SPEA2 and NSGA-II, SPEA2+ showed good results and the effects of the new mechanism were clarified. From these results, it was concluded that SPEA2+ is a good algorithm for multi-objective optimization problems.

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Author information

Authors and Affiliations

  1. Graduate School, Department of Knowledge Engineering and Computer Sciences, Doshisha University, 610-0321, Kyoto, Japan

    Mifa Kim

  2. Department of Knowledge Engineering and Computer Sciences, Doshisha University, 610-0321, Kyoto, Japan

    Tomoyuki Hiroyasu & Mitsunori Miki

  3. Department of Computer Science Faculty of Science & Engineering, Ritsumeikan University, Shiga, Japan

    Shinya Watanabe

Authors
  1. Mifa Kim
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  2. Tomoyuki Hiroyasu
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  3. Mitsunori Miki
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  4. Shinya Watanabe
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Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Xin Yao

  2. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund K. Burke

  3. Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20018, San Sebastian, Donostia, Spain

    José A. Lozano

  4. University of the West of England, Bristol, United Kingdom

    Jim Smith

  5. GeNeura Team, Depto. Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Juan Julián Merelo-Guervós

  6. School of Computer Science, The University of Birmingham, B15 1TT, Edgbaston, Birmingham, UK

    John A. Bullinaria

  7. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, Great Britain

    Jonathan E. Rowe

  8. School of Computer Science, University of Birmingham, United Kingdom

    Peter Tiňo

  9. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    Ata Kabán

  10. Faculty of Computer Science, Dortmund University of Technology, 44221, Dortmund, Germany

    Hans-Paul Schwefel

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© 2004 Springer-Verlag Berlin Heidelberg

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Kim, M., Hiroyasu, T., Miki, M., Watanabe, S. (2004). SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_75

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  • DOI: https://doi.org/10.1007/978-3-540-30217-9_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23092-2

  • Online ISBN: 978-3-540-30217-9

  • eBook Packages: Springer Book Archive

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