Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes

Dynamic optimization has become an increasingly important aspect of chemical processes in the past few decades. To solve such chemical dynamic optimization problems (DOPs) effectively, we put forward a modified differential evolution algorithm named XADE in this paper, which integrates the self-adaptive principle and multiple mutation strategies. In XADE, four mutation strategies with different characteristics are introduced instead of using a single strategy. Meanwhile, the mutation strategies and DE’s two control parameters are gradually adjusted adaptively based on the knowledge learned from the previous searches in generating improved solutions. The advantageous performance of XADE is validated by comparisons with several state-of-the-art adaptive DE variants on 24 complex test instances. Experimental results show that XADE is an effective approach to solving global numerical optimization problems. Moreover, the effectiveness of XADE is validated by applying the approach to 4 real-world complex DOPs with different characteristic in the chemical engineering field.