Hybrid Scale Factor Approach Based Differential Evolution Algorithm

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 improved SF in which the Gaussian distribution function and a pliant parameter(E) are introduced in mutation process of DE. The significant advantage of gaussian distribution is full scale searching.