Prediction strategies play a crucial role in dealing with dynamic multi-objective problems with predictability laws. However,for some complex changing environments,the performance of the algorithms is often not high when only a single prediction strategy is used to respond to the environmental changes. Therefore,a dynamic multi-objective optimization algorithm based on a hybrid prediction strategy is proposed. The objective space is segmented using the quantile method to avoid the generation of null regions. Then,according to the information of the median point in the sub-region at different moments,a linear prediction strategy and an oscillating sequence grey prediction strategy are used to generate new individuals,respectively,and a selection strategy based on variable correlation coefficient is designed to determine some individuals of the initial population in the new environment. A proportional strategy based on non-dominant solution is designed to generate some of the new individuals to ensure good population diversity. To demonstrate the effectiveness of the proposed algorithm,three classical comparison algorithms are used to perform simulation experiments on 9 test functions with different dynamic characteristics. The results show that the algorithm has better performance on most dynamic optimization problems.

动态多目标优化  预测  进化算法  灰色模型 

dynamic multi-objective optimization, prediction, evolutionary algorithm, grey model,