Adaptive dynamic environment response based evolutionary algorithm for dynamic multi-objective optimization

In dynamic multi-objective optimization problems (DMOPs), multiple conflicting objectives vary over time. Therefore, a core issue for the DMOPs is to respond the dynamic environments, when the environment change has been detected. To effectively address DMOPs, a novel method, called adaptive dynamic environment response based dynamic multi-objective evolutionary algorithm (ADER-DMOEA), is proposed in this paper. The proposed algorithm can adaptively respond the dynamic environment based on the detected environment change severity. First, a novel environment change severity detection mechanism is developed, where C-metric is introduced to measure the performance of different population obtained by different types of response strategies. Second, according to the measurement results, the algorithm adaptively adopts different strategies for environment response. Therefore, it can ensure that the proposed method has a good initial population for each new environment. The proposed method is compared with three algorithms, and the experiment results have demonstrated that ADER-DMOEA is competitive in dealing with dynamic environments.