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Exploring the Feasible Space Using Constraint Consensus in Solving Constrained Optimization Problems

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Book cover Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

Over the last few years, constraint consensus methods have been used for the movement of infeasible solutions towards feasible space, when solving constrained optimization problem. In this paper, a novel approach is proposed that is based on the concept of constraint consensus to improve feasible individuals, rather than infeasible ones, in which a feasible individual is considered as an infeasible one, if its fitness value is worse than a dynamic reference point. The obtained new solutions are then passed to differential evolution to be evolved. The proposed algorithm has been tested on the CEC2010 benchmark constrained problems. The results demonstrate better performance of the proposed algorithm, in terms of quality of solutions and computational time, in comparison with a standard differential evolution algorithm, as well as a set of state-of-the-art algorithms.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales at Canberra, Canberra, Australia

    Noha M. Hamza, Daryl L. Essam & Ruhul A. Sarker

Authors
  1. Noha M. Hamza
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  2. Daryl L. Essam
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  3. Ruhul A. Sarker
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Corresponding author

Correspondence to Noha M. Hamza .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

Appendix A

Appendix A

See Appendix Table 4.

Table 4. Function values obtained by DE, EFS-CC-DE, εDEag, ECHT-ARMOR-DE and CO-CLIPSO for the CEC2010 (30D) test problems
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Hamza, N.M., Essam, D.L., Sarker, R.A. (2016). Exploring the Feasible Space Using Constraint Consensus in Solving Constrained Optimization Problems. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

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