Dynamic Configuration of Differential Evolution Control Parameters and Operators

摘要: Differential evolution has shown success in solving different optimization problems. However, its performance depends on the control parameters and search operators. Different from existing approaches, this paper, a new framework which dynamically configures appropriate choices of operators is introduced, operator linked to proper combination scaling factor crossover rate. Also, an adaptation population size adopted. The proposed algorithm assessed using well-known set constrained problems with experimental results demonstrating that it superior state-of-the-art algorithms.