Abstract: Aimed at the problems of slow convergence and difficulty in maintaining diversity, a dynamic multi-objective evolutionary algorithm based on Pareto solution set segmentation prediction is proposed. When the environmental change is detected, the Pareto optimal solution obtained from the evolution at the previous moment is sorted according to a sub-objective function  and divided into three segments according to the size of the sub-objective, then the moving direction of the center point of each Pareto solution set is calculated. Each Pareto subset is systematically sampled to obtain the feature points of the Pareto frontier surface, and the linear model is used to predict the next generation population. According to the difficulty of the optimization problem, adaptive random populations are generated around the predicted population to increase the diversity of the population. 
Experiments on the three types of standard test functions show that the algorithm can effectively solve the dynamic multi-objective optimization problem.

Key words: dynamic multi-objective optimization, segmentation prediction, system sampling, guiding individuals