期刊
APPLIED SOFT COMPUTING
卷 61, 期 -, 页码 806-818出版社
ELSEVIER
DOI: 10.1016/j.asoc.2017.08.004
关键词
Evolutionary dynamic multi-objective optimization; Prediction; Center point; Knee point; Adaptive diversity maintenance mechanism
资金
- National Natural Science Foundation of China [61502408, 61673331]
- Education Department Major Project of Hunan Province [17A212]
- CERNET Innovation Project [NGII20150302]
- MOE Key Laboratory of Intelligent Computing and Information Processing
- Science and Technology Plan Project of Hunan Province [2016TP1020]
- Provinces and Cities Joint Foundation Project [2017JJ4001]
In real life, there are many dynamic multi-objective optimization problems which vary over time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto set) with time. In this paper, we propose a novel prediction strategy based on center points and knee points (CKPS) consisting of three mechanisms. First, a method of predicting the non-dominated set based on the forward-looking center points is proposed. Second, the knee point set is introduced to the predicted population to predict accurately the location and distribution of the Pareto front after an environmental change. Finally, an adaptive diversity maintenance strategy is proposed, which can generate some random individuals of the corresponding number according to the degree of difficulty of the problem to maintain the diversity of the population. The proposed strategy is compared with four other state-of-the-art strategies. The experimental results show that CKPS is effective for evolutionary dynamic multi-objective optimization. (C) 2017 Elsevier B.V. All rights reserved.
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