Global Simplex Optimization-A simple and efficient metaheuristic for continuous optimization

Authors:
Akbar Karimi;Patrick Siarry
Affiliations:
Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université Paris-Est Créteil Val-de-Marne, Créteil 94010, France;Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université Paris-Est Créteil Val-de-Marne, Créteil 94010, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

A new hybrid optimization algorithm is proposed for minimization of continuous multi-modal functions. The algorithm called Global Simplex Optimization (GSO) is a population set based Evolutionary Algorithm (EA) incorporating a special multi-stage, stochastic and weighted version of the reflection operator of the classical simplex method. An optional mutation operator has also been tested and then removed from the structure of the final algorithm in favor of simplicity and because of insignificant effect on performance. The promising performance achieved by GSO is demonstrated by comparisons made to some other state-of-the-art global optimization algorithms over a set of conventional benchmark problems.