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An Adaptive Population-based Simplex Method for Continuous Optimization

An Adaptive Population-based Simplex Method for Continuous Optimization

Mahamed G.H. Omran, Maurice Clerc
Copyright: © 2016 |Volume: 7 |Issue: 4 |Pages: 29
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466691599|DOI: 10.4018/IJSIR.2016100102
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MLA

Omran, Mahamed G.H., and Maurice Clerc. "An Adaptive Population-based Simplex Method for Continuous Optimization." IJSIR vol.7, no.4 2016: pp.23-51. http://doi.org/10.4018/IJSIR.2016100102

APA

Omran, M. G. & Clerc, M. (2016). An Adaptive Population-based Simplex Method for Continuous Optimization. International Journal of Swarm Intelligence Research (IJSIR), 7(4), 23-51. http://doi.org/10.4018/IJSIR.2016100102

Chicago

Omran, Mahamed G.H., and Maurice Clerc. "An Adaptive Population-based Simplex Method for Continuous Optimization," International Journal of Swarm Intelligence Research (IJSIR) 7, no.4: 23-51. http://doi.org/10.4018/IJSIR.2016100102

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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.

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