On an evolutionary approach for constrained optimization problem solving

  • Authors:

  • Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Affiliations:

  • School of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy Campus, Canberra 2600, Australia;School of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy Campus, Canberra 2600, Australia;School of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy Campus, Canberra 2600, Australia

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

Over the last few decades, many different evolutionary algorithms have been introduced for solving constrained optimization problems. However, due to the variability of problem characteristics, no single algorithm performs consistently over a range of problems. In this paper, instead of introducing another such algorithm, we propose an evolutionary framework that utilizes existing knowledge to make logical changes for better performance. The algorithmic aspects considered here are: the way of using search operators, dealing with feasibility, setting parameters, and refining solutions. The combined impact of such modifications is significant as has been shown by solving two sets of test problems: (i) a set of 24 test problems that were used for the CEC2006 constrained optimization competition and (ii) a second set of 36 test instances introduced for the CEC2010 constrained optimization competition. The results demonstrate that the proposed algorithm shows better performance in comparison to the state-of-the-art algorithms.