Dynamic Multi-objective Optimization Algorithm Based on Reference Point Prediction

被引：0
|
作者：
Ding J.-L. [1 ]
Yang C.-E. [1 ]
Chen L.-P. [1 ]
Chai T.-Y. [1 ,2 ]
机构：
[1] State Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang
[2] Research Center of Automation, Northeastern University, Shenyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2017年 / 43卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Dynamic optimization; Multi-objective optimization; Prediction; Reference point; Time series;
D O I：
10.16383/j.aas.2017.c150811
中图分类号：
学科分类号：
摘要：
In tracking the moving Pareto front of dynamic multi-objective optimization problem as soon as possible, a new algorithm based on reference point prediction (PDMOP) is proposed. Firstly, PDMOP distributes the past individuals to different time series according to the information of reference point association. Then for these time series, a linear regression model is used to predict the new environment population. At the same time, historical prediction error is added to the current prediction to enhance prediction accuracy, and a Gauss noise is added to every new individual to increase the initialized population diversity. In this way, the algorithm can speed up convergence in the new environment. The results of four benchmark problems and the comparison with other two existing dynamic multi-objective algorithms indicate that the proposed algorithm can maintain better performance in dealing with dynamic multiobjective problems. Copyright © 2017 Acta Automatica Sinica. All rights reserved.
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页码：313 / 320
页数：7
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