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An Adaptive Population-based simplex Method for Continuous Optimization

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2016年第4期7卷 23-51页

作者： Omran, Mahamed G. H. Clerc, Maurice Gulf Univ Sci & Technol Dept Comp Sci Hawally Kuwait

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.

关键词： Continuous Function Optimization low-dimensional simplex evolution Nelder-Mead simplex Population-based Optimization Methods Triangle evolution

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The Adaptive Population-based simplex Method 5

The Adaptive Population-based Simplex Method

引用

Fifth International Conference on Digital Information Processing and Communications (ICDIPC)

作者： Omran, Mahamed G. H. Clerc, Maurice Gulf Univ Sci & Technol Dept Comp Sci POB 7207 Kuwait 32093 Kuwait

ISBN: (纸本)9781467368322

A novel, tuning-free, population-based simplex method for continuous function optimization is proposed. The proposed method, called Adaptive Population-based simplex (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. The approach is easy to code and easy to understand. APS is compared with four state-of-the-art approaches on five real-world problems. The experimental results show that APS generally performs better than the other methods on the test problems.

关键词： Nelder-Mead simplex low-dimensional simplex evolution triangle evolution continuous optimization

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