A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization

This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization.