Dynamic multi-objective gravitational searching algorithm based on decomposition

被引：0
|
作者：
Diao P. [1 ,2 ]
Bi X. [2 ]
Wang Y. [3 ]
机构：
[1] College of Engineering and Technology, Northeast Forestry University, Harbin
[2] College of Information and Communication Engineering, Harbin Engineering University, Harbin
[3] College of Information Engineering, Northeast Dianli University, Jilin
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2018年 / 38卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Dynamic multi-objective optimization; Prediction model; The multi-objective algorithm based on decomposition;
D O I：
10.12011/1000-6788(2018)05-1300-10
中图分类号：
学科分类号：
摘要：
In order to solve dynamic multi-objective optimization problems, a new dynamic multi-objective gravitational searching algorithm which is based on decomposition technique is proposed in this paper. Firstly an environmental monitoring strategy based on the changes of each objective function optimal solution is adapted to monitor the environment. If the environment doesn’t change, we will use the static multi-objective gravitational searching algorithm to solve the problem. If the environment changes, we will use a hybrid prediction model response to changes in the environment. The hybrid prediction model is based on the similarity of the optimal solution of the adjacent sub population and the optimal solution of the same weight vector corresponding to the sub population. Finally, compared with the advanced static multi-objective algorithm and the forecasting method are compared on four test problems. Experimental results suggest that the proposed algorithm has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts. © 2018, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
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收藏
页码：1300 / 1309
页数：9
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