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TRUST-TECH-enhanced differential evolution methodology for box-constrained nonlinear optimisation
by Xuexia Zhang; Hsiao-Dong Chiang; Weirong Chen
International Journal of Bio-Inspired Computation (IJBIC), Vol. 10, No. 1, 2017

 

Abstract: The differential evolution algorithm and its variants have been developed to solve box-constrained optimisation problems with encouraging results. However, differential evolution still suffers from its poor ability to zoom in on promising regions to find a high-quality or global optimal solution. The transformation under stability-retraining equilibrium characterisation (TRUST-TECH) methodology is a systematical and deterministic method to find a set of multiple local optimal solutions. This paper presents a TRUST-TECH-enhanced differential evolution methodology (TT-DEM) to improve the performance of differential evolution method. In the TT-DEM framework, a differential evolution method is carried out to identify promising regions containing a set of high-quality solution or even the global optimal solution, while TRUST-TECH exploits the identified promising regions to compute those high-quality optimal solutions. Following this framework, the original differential evolution (DE) and three adaptive DEs are enhanced by TRUST-TECH. Numerical studies are conducted on several benchmark functions with promising results.

Online publication date: Tue, 18-Jul-2017

 

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