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A prediction strategy based on decision variable analysis for dynamic Multi-objective Optimization

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成果类型:
期刊论文
作者:
Zheng, Jinhua;Zhou, Yubing*;Zou, Juan;Yang, Shengxiang;Ou, Junwei;...
通讯作者:
Zhou, Yubing
作者机构:
[Zhou, Yubing; Zheng, Jinhua; Hu, Yaru; Ou, Junwei; Zou, Juan] Xiangtan Univ, Minist Educ, Key Lab Intelligent Comp & Informat Proc, Xiangtan 411105, Hunan, Peoples R China.
[Zheng, Jinhua] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.
[Yang, Shengxiang] De Montfort Univ, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England.
通讯机构:
[Zhou, Yubing] X
Xiangtan Univ, Minist Educ, Key Lab Intelligent Comp & Informat Proc, Xiangtan 411105, Hunan, Peoples R China.
语种:
英文
关键词:
Dynamic multi-objective optimization;Evolutionary algorithms;Decision Variable Analysis;Adaptive Selection;Diversity
期刊:
Swarm and Evolutionary Computation
ISSN:
2210-6502
年:
2021
卷:
60
页码:
100786
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61502408, 61673331, 61772178, 61403326]; postgraduate research and innovation Project of Hunan Province [XDCX2019B057]
机构署名:
本校为其他机构
院系归属:
物理与电子工程学院
摘要:
Many multi-objective optimization problems in reality are dynamic, requiring the optimization algorithm to quickly track the moving optima after the environment changes. Therefore, response strategies are often used in dynamic multi-objective algorithms to find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on the classification of decision variables, which consists of three steps. After detecting the environment change, the first step is to analyze the influence of each decision variable on individual convergence and distribution in the new environment. The secon...

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