• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2020, Vol. 42 ›› Issue (08): 1454-1462.

• 人工智能与数据挖掘 • 上一篇    下一篇

基于Pareto解集分段预测策略的动态多目标进化算法

马永杰,陈满丽,陈敏   

  1. (西北师范大学物理与电子工程学院,甘肃 兰州 730070)
  • 收稿日期:2019-10-08 修回日期:2020-02-27 接受日期:2020-08-25 出版日期:2020-08-25 发布日期:2020-08-29

A dynamic multi-objective evolutionary algorithm based on Pareto solution set segmentation prediction strategy

MA Yong-jie,CHEN Man-li,CHEN Min   

  1. (College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China)

  • Received:2019-10-08 Revised:2020-02-27 Accepted:2020-08-25 Online:2020-08-25 Published:2020-08-29

摘要: 针对现有的动态多目标优化算法种群收敛速度慢、多样性难以保持等问题,提出了一种基于Pareto解集分段预测策略的动态多目标进化算法BPDMOP。当检测到环境变化时,对前一时刻进化得到的Pareto最优解根据任一子目标函数进行排序,并按照该子目标的大小均分为3段,分别计算出每一段Pareto解集中心点的移动方向;对每一段Pareto子集进行系统抽样得到Pareto前沿面的特征点,
利用线性模型分段预测下一代种群;根据优化问题的难易程度,自适应地在预测的种群周围产生随机个体来增加种群的多样性。
通过对3类标准测试函数的实验表明了该算法能够有效求解动态多目标优化问题。


关键词: 动态多目标优化, 分段预测, 系统抽样, 引导个体

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

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