Global Simplex Optimization-A simple and efficient metaheuristic for continuous optimization
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. (C) 2011 Elsevier Ltd. All rights reserved.
global simplex optimization metaheuristics continuous optimization evolutionary algorithms simplex method global optimization