Dynamic Multiobjective Optimization Using Evolutionary Algorithm with Kalman Filter

Multiobjective optimization is a challenging task, especially in a changing environment. The study on dynamic multiobjective optimization is so far very limited. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. In this paper, a Kalman Filter prediction-based evolutionary algorithm is proposed to solve dynamic multiobjective optimization problems. This prediction model uses historical information to predict for future generations and thus, direct the search towards the Pareto optimal solutions. A scoring scheme is then devised to further enhance the performance by hybridizing the Kalman Filter prediction model with the random re-initialization method. The proposed models are tested and analysis of the experiment results are presented. It is shown that the proposed models are capable of improving the performances, as compared to using random re-initialization method alone. The study also suggests that additional features could be added to the proposed models for improvements and much more research in this field is still needed.