A dual prediction strategy with inverse model for evolutionary dynamic multiobjective optimization

In practical applications and daily life, dynamic multiobjective optimization problems (DMOPs) are ubiquitous. The purpose of dealing with DMOPs is to track moving Pareto Front (PF) and find a series of Pareto Set (PS) at different times. Prediction-based strategies improve the performance of multiobjective evolutionary algorithms in dynamic environments. However, how to ensure the accuracy of prediction models is always a challenge. In this study, a dual prediction strategy with inverse model (DPIM) is developed, to alleviate the negative impact of inaccurate prediction. When a change is confirmed, DPIM responses to it by predicting the individuals in the objective space. Furthermore, the inverse model is established to connect the decision space with the objective space, which can guide the search for promising decision areas. Specifically, the inverse model is also predicted to minimize the error in the process of mapping the population from the objective space back to the decision space. The effectiveness of the proposed DPIM is proved by comparison with four effective DMOEAs on 14 benchmark problems with various real-word scenarios. The experimental results show that DPIM can obtain high-quality populations with good convergence and distribution in dynamic environments.