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A Simple Ranking and Selection for Constrained Evolutionary Optimization

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with four other evolutionary based methods, using nine benchmark problems from the relevant literature, it shows very encouraging performance.

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Author information

Authors and Affiliations

  1. School of ITEE, University of New South Wales, ADFA, Canberra, 2600, Australia

    Ehab Z. Elfeky, Ruhul A. Sarker & Daryl L. Essam

Authors
  1. Ehab Z. Elfeky
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  2. Ruhul A. Sarker
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  3. Daryl L. Essam
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Elfeky, E.Z., Sarker, R.A., Essam, D.L. (2006). A Simple Ranking and Selection for Constrained Evolutionary Optimization. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_68

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  • DOI: https://doi.org/10.1007/11903697_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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