Multi-region trend prediction strategy with online sequential extreme learning machine for dynamic multi-objective optimization

Dynamic multi-objective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, requiring dynamic multi-objective algorithms (DMOAs) to track changing Pareto-optimal fronts. In recent decade, prediction-based DMOAs have shown promise in handling DMOPs. However, in existing prediction-based DMOAs some specific solutions in a small number of prior environments are generally used. Consequently, it is difficult for these DMOAs to capture Pareto-optimal set (POS) changes accurately. Besides, gaps may exist in some objective subspaces due to uneven population distribution, causing a difficulty in searching these subspaces. Faced with such difficulties, this article proposes a multi-region trend prediction strategy-based dynamic multi-objective evolutionary algorithm (MTPS-DMOEA) to handle DMOPs. MTPS-DMOEA divides the objective space into multiple subspaces and predicts POS moving trends through the use of POS center points from multiple objective subspaces, which contributes to accurately capturing POS changes. In MTPS-DMOEA, the parameters of the prediction model are continuously updated via online sequential extreme learning machine, facilitating the adequate utilization of useful information in historical environments and hence the enhancement of the generalization performance for the prediction. To fill gaps in some objective subspaces, MTPS-DMOEA introduces diverse solutions generated from the previous POS in adjacent subspaces. We compare the proposed MTPS-DMOEA with six state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results demonstrate the excellent performance of MTPS-DMOEA in handling DMOPs.