Classification Multi-Strategy Predictive Dynamic Multi-Objective Optimization with Pareto Set Rotation

In order to solve the dynamic multi-objective optimization problem of Pareto set (PS) rotation more effectively, this paper proposes a classification multi- strategy prediction method based on PS rotation (RFM). Firstly, the rotation types of PS are divided into PS center point rotation, PS origin rotation and non-standard rotation. Then, the appropriate prediction model is adaptively selected for the above different PS rotation types, and the time series of different point sets is established to provide the initial population for the subsequent evolution. Finally, the random population generated by Latin hypercube strategy (LHS) is introduced to construct a new population together with the above predicted population to ensure the diversity of the population. In order to verify the effectiveness of the algorithm, the RFM algorithm is compared with DNSGA-II, PPS, SPPS and MMP algorithms on eight standard dynamic test functions. The experimental results show that the RFM algorithm achieves six optimal IGD values, seven optimal SP values and three optimal MS values, which proves that the RFM algorithm can solve the dynamic multi-objective optimization problem based on PS rotation more effectively. At the same time, the generality of the RFM algorithm is verified by experiments on the FDA series of functions. The experimental results show that the algorithm still has better performance in dealing with non-rotating dynamic multi-objective optimization problems. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.