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Incrementing Multi-objective Evolutionary Algorithms: Performance Studies and Comparisons

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

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Abstract

This paper addresses the issue by presenting a novel “incrementing” multi-objective evolutionary algorithm (IMOEA) with dynamic population size that is adaptively computed according to the on-line discovered trade-off surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine-tuning for broader neighborhood exploration to achieve better convergence as well as discovering any gaps or missing trade-off regions at each generation. Comparative studies with other multi-objective (MO) optimization are performed on benchmark problem. The new suggested quantitative measures together with other well-known measures are employed to access and compare their performances statistically.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, 10 Kent Ridge, 119260, Crescent, Singapore

    K. C. Tan, T. H. Lee & E. F. Khor

Authors
  1. K. C. Tan
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  2. T. H. Lee
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  3. E. F. Khor
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Tan, K.C., Lee, T.H., Khor, E.F. (2001). Incrementing Multi-objective Evolutionary Algorithms: Performance Studies and Comparisons. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_8

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  • DOI: https://doi.org/10.1007/3-540-44719-9_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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