Gaussian scale factor based differential evolution

Abstract

Differential Evolution (DE) is a easy and basic populace based probabilistic approach for global optimization. It has reportedly outperformed very well as compared to different nature inspired algorithms like Genetic algorithm (GA), Particle swarm optimization (PSO) when tested over both benchmark and real world problems. In DE algorithm there are crossover rate (CR), and scale factor (SF) are two control parameters, which play a crucial role to retain the proper equilibrium betwixt intensification and diversification abilities. But, DE, like other probabilistic optimization approaches, sometimes behave prematurely in convergence. Therefore, to retain the proper equilibrium betwixt exploitation and exploration capabilities, we introduce a modified SF in which the Gaussian distribution function and a flexible parameter (N) are introduced in mutation process of DE. The significant advantage of Gaussian distribution is full scale searching. The resulting algorithm is named as Gaussian scale factor based differential evolution GSFDE algorithm. To prove the efficiency and efficacy of GSFDE, it is tested over 20 benchmark optimization problems and the results are compared with the basic DE and advanced variants of DE namely, Gbest-guided differential evolution (Gbest DE), L‘evy Flight based Local Search in Differential Evolution (LFDE) and some swarm intelligence based algorithms like Modified artificial bee colony algorithm (MABC), Best-so-far ABC (BSFABC), Particle swarm optimization (PSO), and spider monkey optimization (SMO). The obtained results depict that GSFDE is a competent in the field of optimization.