Differential Evolution Improved with Adaptive Control Parameters and Double Mutation Strategies.

Recently, differential evolution (DE) algorithm has attracted more and more attention as an excellent and effective approach for solving numerical optimization problems. However, it is difficult to set suitable mutation strategies and control parameters. In order to solve this problem, in this paper a dynamic adaptive double-model differential evolution (DADDE) algorithm for global numerical optimization is proposed, and dynamic random search (DRS) strategy is introduced to enhance global search capability of the algorithm. The simulation results of ten benchmark show that the proposed DADDE algorithm is better than several other intelligent optimization algorithms.