Prediction-based dynamic constrained multi-objective optimization via temporal–spatial response error suppression

Prediction-based dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) have received increasing attention for their ability to leverage historical information to provide high-quality initial populations for new environments. However, these algorithms suffer from prediction bias when the learned trend mismatches the actual environmental change. Such prediction bias contributes to the response error, the deviation between the population and the target constrained Pareto set (CPS) after the dynamic response. This response error persists into the static optimization, degrading both convergence and coverage under a limited computational budget. To address this issue, this paper proposes a prediction-based DCMOEA based on temporal–spatial response error suppression. Along the temporal dimension, a feedback-corrected migration strategy with Kalman filtering is designed to compensate for the prediction bias accumulated over the environmental change sequence, suppressing the response error at its source. A theoretical analysis establishes a sufficient condition under which the feedback correction reduces the centroid prediction error. Along the spatial dimension, a subspace anchor-based environmental selection strategy is introduced to preserve one representative solution in each non-empty subspace, preventing solutions in regions affected by the response error from being eliminated during environmental selection. Experimental results on two benchmark suites demonstrate that the proposed algorithm significantly outperforms seven state-of-the-art DCMOEAs in finding Pareto optimal solutions on the majority of instances. The results also demonstrate that addressing the response error is a key factor for improving the performance of prediction-based DCMOEAs, offering a new perspective for designing prediction-based DCMOEAs.