Dynamic Constrained Multiobjective Evolutionary Algorithm With Multipopulation Prediction and Dynamic Fusion Ranking

Dynamic constrained multiobjective optimization problems (DCMOPs) are widely existed in real-world applications and emerged as a prominent research focus in the evolutionary computation community. Current studies on DCMOPs face two main challenges: limited accuracy in population prediction, and a lack of effective strategies to improve static optimizer performance. To tackle these challenges, this paper proposes a dynamic constrained multiobjective evolutionary algorithm based on multipopulation prediction and dynamic fusion ranking. Specifically, an efficient computer-vision-inspired point set registration method, named coherent point drift, is introduced to align individuals across successive environments. With the correspondences between two environments, the solution trajectory tracking problem is transformed as a point set registration problem. Based on the observed trajectory of solutions, the Pareto-optimal set or Pareto-optimal front in the new environment can be predicted. Additionally, this paper highlights the importance of task-specific multipopulation prediction. After analysis the specific tasks of each population, different initial populations tailored to the tasks are predicted by the proposed prediction method. Finally, a dynamic fusion based two-ranking environmental selection strategy is proposed for the auxiliary task. This strategy dynamically integrates experience-based and constraint-based approaches, improving the auxiliary population evolutionary efficiency and its alignment with the main task. The superiority of proposed algorithm is validated through extensive experiments on a series of benchmark problems and a real-world raw ore allocation problem.