关键词: 动态多目标进化算法, 权重向量, 聚类, 差分模型, 种群预测

Abstract:

There are many Dynamic Multiobjective Optimization Problems （DMOPs） in real life. For such problems， when the environment changes， Dynamic Multi-Objective Evolutionary Algorithm （DMOEA） is required to track the Pareto Front （PF） or Pareto Set （PS） quickly and accurately under the new environment. Aiming at the problem of poor performance of the existing algorithms on population prediction， a dynamic multi-objective optimization algorithm based on Weight Vector Clustering Prediction （WVCP） was proposed. Firstly， the uniform weight vectors were generated in the target space， and the individuals in the population were clustered. According to the clustering results， the distribution of the population was analyzed. Secondly， a time series was established for the center points of clustered individuals. For the same weight vector， the corresponding coping strategies were adopted to supplement individuals according to different clustering situations. If there were cluster centers at all adjacent moments， the difference model was used to predict individuals in the new environment. If there was no cluster center at a certain moment， the centroid of the cluster centers of adjacent weight vectors was used as the cluster center at that moment， and then the difference model was used to predict individuals. In this way， the problem of poor population distribution was solved effectively， and the accuracy of prediction was improved at the same time. Finally， the introduction of individual supplement strategy was beneficial to make full use of historical information. In order to verify the performance of the proposed algorithm， simulation comparison of this algorithm and four representative algorithms was carried out. Experimental results show that the proposed algorithm can solve DMOPs well.

Key words: Dynamic Multi-Objective Evolutionary Algorithm (DMOEA), weight vector, clustering, difference model, population prediction