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大规模黑箱优化问题元启发式求解方法研究进展

江璞玉 刘均 周奇 程远胜

江璞玉, 刘均, 周奇, 等. 大规模黑箱优化问题元启发式求解方法研究进展[J]. 中国舰船研究, 2021, 16(4): 1–18 doi: 10.19693/j.issn.1673-3185.02248
引用本文: 江璞玉, 刘均, 周奇, 等. 大规模黑箱优化问题元启发式求解方法研究进展[J]. 中国舰船研究, 2021, 16(4): 1–18 doi: 10.19693/j.issn.1673-3185.02248
JIANG P Y, LIU J, ZHOU Q, et al. Advances in meta-heuristic methods for large-scale black-box optimization problems[J]. Chinese Journal of Ship Research, 2021, 16(4): 1–18 doi: 10.19693/j.issn.1673-3185.02248
Citation: JIANG P Y, LIU J, ZHOU Q, et al. Advances in meta-heuristic methods for large-scale black-box optimization problems[J]. Chinese Journal of Ship Research, 2021, 16(4): 1–18 doi: 10.19693/j.issn.1673-3185.02248

大规模黑箱优化问题元启发式求解方法研究进展

doi: 10.19693/j.issn.1673-3185.02248
详细信息
    作者简介:

    江璞玉,男,1994年生,博士生。研究方向:进化计算,代理模型辅助的进化计算。E-mail:puyujiang1994@qq.com

    刘均,男,1981年生,博士,副教授,博士生导师。研究方向:结构分析与优化,结构抗爆抗冲击,结构振动与控制。E-mail:hustlj@hust.edu.cn

    周奇,男,1990年生,博士,副教授,博士生导师。研究方向:装备智能优化设计,代理模型理论与方法。E-mail:qizhouhust@gmail.com

    程远胜,男,1962年生,博士,教授,博士生导师。研究方向:结构分析与轻量化设计,结构冲击动力学与防护设计,基于代理模型的优化方法。E-mail:yscheng@hust.edu.cn

    通信作者:

    程远胜

  • 中图分类号: U662.9

Advances in meta-heuristic methods for large-scale black-box optimization problems

  • 摘要: 大型复杂工程装备的优化设计通常为高复杂度、高维度的优化问题,即所谓的大规模黑箱优化问题,其特点是目标函数和/或约束函数解析式不可知且设计变量维度很高。近年来,大规模黑箱优化问题在各领域引起了学者们的兴趣,而元启发式算法被认为是求解该问题的有效方法。为此,全面总结了近年来求解该问题的元启发式算法的研究进展,包括使用与不使用分解策略的元启发式算法,以及处理大规模昂贵优化问题的代理模型辅助元启发式算法,并指出了针对此问题的元启发式求解方法未来可能的研究方向。
  • 图  1  元启发式算法流程图

    Figure  1.  Flowchart of meta-heuristic algorithm

    图  2  差分进化算法流程图

    Figure  2.  Flowchart of differential evolution algorithm

    图  3  粒子群算法流程

    Figure  3.  Flowchart of particle swarm optimization

    图  4  合作协同进化算法框架

    Figure  4.  Framework of cooperative co-evolutionary algorithm

    图  5  代理模型与元启发式算法交互示意图

    Figure  5.  Diagram of interaction between surrogate modle and meta-heuristic algorithm

    表  1  大规模黑箱优化问题元启发式算法研究文献汇总

    Table  1.   Summary of research advances using meta-heuristic algorithms for LBOPs

    是否基于分解策略使用的优化框架典型文献
    DE算法[12-28]
    PSO算法[29-39]
    其他及混合型算法[40-49]
    合作协同进化算法(CCEA)[50-105]
    非CCEA算法[106-118]
    下载: 导出CSV

    表  2  合作协同进化算法研究文献汇总

    Table  2.   Summary of research advances of CCEA

    研究内容典型文献
    分组策略[50-73, 128]
    合作者选择策略[50-51, 61, 74-91]
    适应度评价策略[88-89, 92-100]
    计算资源分配策略[50, 101-105]
    下载: 导出CSV

    表  3  代理模型辅助元启发式算法的文献分类

    Table  3.   Summary of surrogate model assisted meta-heuristic algorithms

    是否基于
    分解策略
    典型文献
    RBF模型GP模型
    [141-146][138, 140, 145, 147]
    [148-151][151]
    下载: 导出CSV
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  • 收稿日期:  2020-12-31
  • 修回日期:  2021-03-29
  • 网络出版日期:  2021-07-16
  • 刊出日期:  2021-08-10

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