A Correlation-Guided Layered Prediction Approach for Evolutionary Dynamic Multiobjective Optimization

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary algorithms, the historical moving directions of some special points along the Pareto front, such as the center and knee points, are widely employed to predict the Pareto-optimal solutions (POSs). However, special points may be impacted by certain individuals with a large direction deviation, and thus, mislead the tracking of dynamic POS. To solve this issue, a correlation-guided layered prediction approach for solving DMOPs is proposed in this article, where multiple prediction models are integrated by considering the correlation of individuals' moving directions. To be specific, the population is clustered into three subpopulations (i.e., high, mid, and low correlation) by correlation analysis to perform different prediction behaviors. The high correlation subpopulation aims to predict the moving direction via a linear prediction model. The mid correlation subpopulation is devoted to predicting the manifold change of POS by self-adaptively using the direction and length correction models. The diversity preservation is considered by the low correlation subpopulation. While the three subpopulations focus on different optimization tasks, they also cooperate to track the dynamic POS. The comprehensive experimental results on a variety of benchmark test problems demonstrate the superiority of the proposed approach, as compared with some state-of-the-art prediction-based dynamic multiobjective algorithms.