A diversity introduction strategy based on change intensity for evolutionary dynamic multiobjective optimization

Many real-world problems can be modeled as dynamic multiobjective optimization ones with several competing objectives, which requires an optimization algorithm to track the movement of Pareto front over time. This paper proposes a novel dynamic diversity introduction strategy based on change intensity to improve the performance of dynamic multiobjective optimization based on evolutionary algorithm (DMOEA). In this proposed method, the information generated during evolution is recorded in preparation for evaluating the change intensity. Then, by comparing the evaluated intensity with the inherent intensity, the introduction ratio can be determined by that greater one. Two diversity introduction strategies are utilized to keep the balance between convergence and diversity when environmental change is detected. An improved inverse modeling is used for those drastic changes, while partial solutions random initialization is utilized for these mild ones. We compare the proposed algorithm with four existing DMOEAs on a variety of test instances. The experimental results show that the proposed algorithm exhibits better search performance.