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APS 9: an improved adaptive population-based simplex method for real-world engineering optimization problems

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Abstract

The adaptive population-based simplex (APS) algorithm is a recently-proposed optimization method for solving continuous optimization problems. In this paper, a new variant of APS, referred to as APS 9, is proposed to solve engineering optimization problems. APS 9 still follows the main structure of APS where three strategies (i.e., reflection, contraction and local search) can be used to improve the population of solutions. However, the three strategies have been significantly modified and the rules for applying them have been revised. A stagnation detection mechanism and duplicates removal step have been added. The proposed method is compared with the winners of the IEEE CEC 2005 and CEC 2011 competitions on the 22 CEC 2011 problems. The results show the superiority of APS 9 compared to the other two methods. Moreover, APS 9 has been compared with two recent optimization methods on the same test bed. The limitations of the CEC 2011 competition are also discussed and new rules that are more engineering-friendly are proposed.

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Notes

  1. 1.

    CMA-ES MATLAB code is downloaded from (https://www.lri.fr/~hansen/cmaes_inmatlab.html https://www.lri.fr/~hansen/cmaes_inmatlab.html). We have used the latest version (April 2012), which should be better (or at least as good as) than that which won the CEC 2005 competition.

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Acknowledgements

This work was supported by the Kuwait Foundation for the Advancement of Science (KFAS) under Grant P114-18SM-02.

The authors would like to thank Dr. Saber Elsayed for providing the MATLAB code of GA-MPC. Moreover, the authors would like to thank the anonymous reviewers for their valuable comments.

Author information

Correspondence to Mahamed G. H. Omran.

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Omran, M.G.H., Clerc, M. APS 9: an improved adaptive population-based simplex method for real-world engineering optimization problems. Appl Intell 48, 1596–1608 (2018). https://doi.org/10.1007/s10489-017-1015-z

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Keywords

  • Adaptive population-based simplex
  • Metaheuristics
  • Real-world optimization
  • Continuous optimization
  • IEEE CEC 2011 competition