A Stable Matching-Based Selection and Memory Enhanced MOEA/D for Evolutionary Dynamic Multiobjective Optimization

In the real world, dynamic changes may occur during multi-objective optimization. In those situations, it is vital to track the time-varying Pareto optimal set over time. This paper is to integrate a memory-enhanced multi-objective evolutionary algorithm based on decomposition (denoted by dMOEA/D-M) with a simple and effective stable matching (STM) model (denoted by dMOEA/D-STM). MOEA/D is an effective algorithm for optimizing static multi-objective problems. For adapting to the dynamic changes, firstly, an improved environment detector is presented. Then, memory and matching skills is designed to address the difficulties of re-initialization. The STM model, which originates from economics, guides the re-initialization in dMOEA/D-STM. Empirical experiments prove the effectiveness of the memory strategy and STM model.