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An Adaptive Population-based Simplex Method for Continuous Optimization

Author

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  • Mahamed G.H. Omran

    (Department of Computer Science, Gulf University for Science and Technology, Hawally, Kuwait)

  • Maurice Clerc

    (Independent Consultant, Groisy, France)

Abstract

This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.

Suggested Citation

  • Mahamed G.H. Omran & Maurice Clerc, 2016. "An Adaptive Population-based Simplex Method for Continuous Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 7(4), pages 23-51, October.
    • Handle: RePEc:igg:jsir00:v:7:y:2016:i:4:p:23-51
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