Improving Differential Evolution Algorithm with Activation Strategy

Evolutionary Computation (EC) provides high performance on real world optimization problems such as scheduling, resource distribution, portfolio optimization etc. Differential (DE) algorithm was first reported in 1995. Based on the characteristics of simple structure, high accuracy and efficiency, and the requirement of fewer parameters, DE has received significant attention from researchers. It has been applied to numerous fields and performs much better than other evolutionary computation.Although DE shows powerful performance, but it has attach importance to the drawbacks of unstable convergence, breakaway the solution space, and the common defect of evolution computation dropping into regional optimum. In this study, inspired from Genetic Algorithm (GA), we attempt to improve the traditional differential evolution algorithm and propose a novel algorithm Strategy Differential Evolution (ASDE). Based on import the Activated Strategy (AS) intensified the structure of traditional DE for enhancing the accuracy and efficiency again