Differential evolution with stochastic fractal search algorithm for global numerical optimization

Abstract

Evolutionary algorithms are successfully developed to handle the challenges in solving optimization problems with complex landscapes. Differential evolution proves its efficiency as a powerful evolutionary algorithm to solve complex optimization problems with diverse characteristics. In this paper, we aim at designing an enhanced evolutionary algorithm that embeds Differential Evolution in Stochastic Fractal Search. Stochastic Fractal Search is developed recently as a powerful metaheuristic algorithm that imitates the natural phenomenon of growth and uses the diffusion process based on random fractals. In this paper, we introduce a new adjustment to the Diffusion Process of Stochastic Fractal Search. The proposed algorithm namely, SFS-DPDE-GW, uses Differential Evolution in the Diffusion Process along with the Gaussian Walks to enhance the search. To validate the performance of our algorithm, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The proposed combination clearly enhances the performance of Stochastic Fractal Search and increases the efficiency of the update process which was incorporated after Diffusion process. Comparative studies show that the new algorithm has a superior performance compared to the original Stochastic Fractal Search and other recent state-of-the-art algorithms.