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作者:

Li, Sanyi (Li, Sanyi.) | Yang, Shengxiang (Yang, Shengxiang.) | Wang, Yanfeng (Wang, Yanfeng.) | Yue, Weichao (Yue, Weichao.) | Qiao, Junfei (Qiao, Junfei.)

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EI Scopus SCIE

摘要:

This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization.

关键词:

Population prediction Dynamic multi-objective optimization Modular neural network

作者机构:

  • [ 1 ] [Li, Sanyi]Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China
  • [ 2 ] [Wang, Yanfeng]Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China
  • [ 3 ] [Yue, Weichao]Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China
  • [ 4 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Yang, Shengxiang]De Montfort Univ, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England

通讯作者信息:

  • [Yang, Shengxiang]De Montfort Univ, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England

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来源 :

SWARM AND EVOLUTIONARY COMPUTATION

ISSN: 2210-6502

年份: 2021

卷: 62

1 0 . 0 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:87

JCR分区:1

被引次数:

WoS核心集被引频次: 16

SCOPUS被引频次: 17

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

近30日浏览量: 1

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