Enhancing Dynamic Constrained Multiobjective Optimization With Multicenters-Based Prediction
提出一种多中心预测策略，通过聚类历史最优解并预测新环境中心，生成分布良好的初始种群，以快速穿越不可行区域并准确跟踪动态约束多目标优化问题的帕累托最优解集。
Dynamic constrained multiobjective optimization problems (DCMOPs) involve complex changes in objective functions and constraints over time. These changes challenge most existing algorithms to quickly cross infeasible regions and accurately track the changing Pareto optimal set (POS) and Pareto optimal front (POF). To address this issue, this article presents a multicenters-based prediction strategy, termed FCP, for solving DCMOPs more effectively. First, we introduce a penalty function to cluster the historical optimal solutions, thereby obtaining multicenters of these solutions. These centers can roughly represent the distribution of different clusters in POS. Then, we predict cluster centers of the new environment’s POS by calculating the distance of centers from the preceding two environments. The prediction strategy can handle the change of POS caused by constraints thereby improving the accuracy of prediction. Finally, a proposed population generator calculates the distances between new centers and utilizes information from these centers to predict a well-distributed initial population. Comprehensive studies on widely used benchmark problems demonstrate that our proposed algorithm is very competitive in dealing with DCMOPs compared with seven state-of-the-art algorithms. Meanwhile, to validate the proposed prediction strategy, it is embedded into the static constraints handling techniques from other DCMOEAs to solving DCMOPs and the experimental results indicate that FCP is superior in generating initial population.