A prediction strategy based on guide-individual for dynamic multi-objective optimization

被引：0

作者：

Zheng, Jin-Hua ^{[1
,2
]}

Peng, Zhou ^{[1
,2
]}

Zou, Juan ^{[1
,2
]}

Shen, Rui-Min ^{[1
,2
]}

机构：

[1] Institute of Information Engineering, Xiangtan University, Xiangtan, 411105, Hunan

[2] Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Xiangtan, 411105, Hunan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 09期

关键词：

Dynamic multi-objective optimization; Evolutionary algorithms; Guide-individual; Prediction;

D O I：

10.3969/j.issn.0372-2112.2015.09.021

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Many real-world problems are dynamic multi-objective optimization problem. This kind of problem has multiple objectives, and these objectives change constantly due to the influence of environment. In this paper, a prediction strategy based on guide-individual (GIPS) is proposed. When environment changes, by recording the different center positions between populations in the initial environment and the ones evolving independently after a short time, GIPS predicts the direction of the optimal solutions. Moreover, from uniformly distributed individuals detected in the prediction direction, GIPS selects a bunch of non-dominated individuals as the guide-individuals for the current environment. In addition, the proposed strategy randomly generates a number of associated guide-individuals within a small area in order to avoid population to be trapped into local optimum. Compared with two state-of-the-art prediction-based dynamic multi-objective optimization algorithms, GIPS show faster response to the environmental changes. ©, 2015, Chinese Institute of Electronics. All right reserved.

引用

页码：1816 / 1825

页数：9

共 21 条

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