A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment

Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms.