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进化计算在复杂机电系统设计自动化中的应用综述

范衠 朱贵杰 李文姬 游煜根 李晓明 林培涵 辛斌

范衠, 朱贵杰, 李文姬, 游煜根, 李晓明, 林培涵, 辛斌. 进化计算在复杂机电系统设计自动化中的应用综述. 自动化学报, 2020, 41(x): 1−21 doi: 10.16383/j.aas.c190767
引用本文: 范衠, 朱贵杰, 李文姬, 游煜根, 李晓明, 林培涵, 辛斌. 进化计算在复杂机电系统设计自动化中的应用综述. 自动化学报, 2020, 41(x): 1−21 doi: 10.16383/j.aas.c190767
Fan Zhun, Zhu Gui-Jie, Li Wen-Ji, You Yu-Gen, Li Xiao-Ming, Lin Pei-Han, Xin Bin. Applications of evolutionary computation in the design automation of complex mechatronic system: A survey. Acta Automatica Sinica, 2020, 41(x): 1−21 doi: 10.16383/j.aas.c190767
Citation: Fan Zhun, Zhu Gui-Jie, Li Wen-Ji, You Yu-Gen, Li Xiao-Ming, Lin Pei-Han, Xin Bin. Applications of evolutionary computation in the design automation of complex mechatronic system: A survey. Acta Automatica Sinica, 2020, 41(x): 1−21 doi: 10.16383/j.aas.c190767

进化计算在复杂机电系统设计自动化中的应用综述

doi: 10.16383/j.aas.c190767
基金项目: 国家自然科学基金委优秀青年科学基金(61822304), 广东省科技计划项目(180917144960530), 广东省教委科研项(2017KZDXM032), 广东省国际科技合作平台项目(2019A 050519008)资助
详细信息
    作者简介:

    范衠:博士, 汕头大学工学院教授. 2004年获得美国密歇根州立大学博士学位. 主要研究方向为机电系统设计自动化, 机器人, 进化计算.E-mail: zfan@stu.edu.cn

    朱贵杰:汕头大学工学院博士研究生. 主要研究方向为机电系统设计自动化与智能机器人系统.E-mail: 16gjzhu@stu.edu.cn

    李文姬:汕头大学工学院博士研究生.主要研究方向为约束多目标进化计算.E-mail: wenji_li@126.com

    游煜根:2019年获得汕头大学工学院硕士学位. 主要研究方向为多目标进化优化, 机器人优化设计与控制.Email: 12ygyou@stu.edu.cn

    李晓明:汕头大学工学院硕士研究生.主要研究方向为智能机器人系统优化控制.E-mail: 19xmli@stu.edu.cn

    林培涵:汕头大学工学院硕士研究生. 主要研究方向为机器人结构设计和优化.E-mail: 19phlin@stu.edu.cn

    辛斌:博士, 北京理工大学自动化学院教授. 2012年获得北京理工大学自动化学院博士学位. 主要研究方向为计算智能, 多机器人系统. 本文通信作者.E-mail: brucebin@bit.edu.cn

Applications of Evolutionary Computation in the Design Automation of Complex Mechatronic System: A Survey

Funds: Supported by National Outstanding Youth Talents Support Program (61822304), Science and Technology Planning Project of Guangdong Province of China (180917144960530), Project of Educational Commission of Guangdong Province of China (2017KZDXM032), International Technology Cooperation Platform Project of Guangdong Province of China (2019A 050519008)
  • 摘要: 复杂机电系统设计自动化是知识自动化的一个重要分支, 在机器人系统设计、高档数控机床设计、智能装备系统设计等方面具有重要的研究意义和应用价值. 本文对进化计算在复杂机电系统设计自动化中的应用进行了综述. 首先, 介绍了几种常用进化计算方法及其优点; 其次, 对进化计算在电子系统、微机电系统和复杂机电系统三个领域的设计自动化进行了较为系统且全面的总结. 然后, 以一类典型的复杂机电系统—机器人系统的设计自动化为代表, 对进化计算在机器人系统设计自动化的研究发展进行了讨论. 最后, 针对进化计算在复杂机电系统设计自动化中存在的共性关键问题进行了讨论与展望.
  • 图  1  智能设计与制造平台中各部分间的关系图

    Fig.  1  The relationship between the components of the intelligent design and manufacturing platform

    图  2  具有非凸Pareto前沿的优化问题的示意图

    Fig.  2  A non-convex Pareto front of a multi-objective optimization problem

    图  3  进化计算的基本流程框架

    Fig.  3  Basic flow framework of evolutionary computation

    图  4  不同系统的设计自动化之间的关系

    Fig.  4  The relationships of design automation of different systems

    图  5  不同应用领域的谐振器键合图表示

    Fig.  5  One bond graph represents resonators in different application domains

    图  6  基因型-表现型的映射实例

    Fig.  6  An instance of genotype-phenotype mapping

    图  7  机电系统的进化设计框架

    Fig.  7  The framework of evolutionary synthesis of mechatronic systems

    图  8  结构可优化抗扰控制器的设计框架[107]

    Fig.  8  The framework of disturbance rejection controllers with optimized structures[107]

    表  1  不同设计方法的对比[80]

    Table  1  Comparison of various design approaches[80]

    特性 设计方法
    BG GA GP BG/GA BG/GP
    多能域
    拓扑搜索
    进化过程
    自动综合
    最优化设计
    有效评估
    下载: 导出CSV

    表  2  机电系统设计自动化中设计方法的总结

    Table  2  A survey of design methods for MDA

    序号 设计方法 参考文献
    1 非线性规划算法 Yin等[116]
    2 遗传算法 Zhang等[40], 解光军和肖晗[44], Nabavi和Zhang等[54-56], Li等[83], Yousfi等[89], 陈启鹏等[95]
    3 进化策略算法 Kim[34], Zega和Frangi等[46]
    4 文化基因算法 Arab等[86]
    5 差分进化算法 Zheng等[47], Ak等[49], Rodíguez-Molina等[84], Ochoa等[88], Zheng等[96-97]
    6 改进差分进化算法 Fan等[18], 展娇杨等[107]
    7 粒子群算法 Poddar等[38], Ye等[83], 王福斌等[93]
    8 人工蜂群算法 Caraveo等[85], Zhang等[94]
    9 基因编程 Koza等[16], [98-101], Vasicek和Sekanina[36]
    10 键合图+遗传算法 Tay等[78]
    11 遗传算法+粒子群算法 Lapa和Cpalka[103]
    12 遗传算法+模拟退火算法 Shokouhifar等[37], Li等[90]
    13 差分进化算法+粒子群算法 Moharam等[91]
    14 键合图+基因编程 Dupuis等[43], Seo等[79], Fan等[60], [81-82], Wang等[20], [111]
    15 遗传算法+基于梯度的局部优化算法 Zhang[66]
    16 遗传算法+基因编程 Fan等[60], Bruijnen等[102]
    17 混合键合图+基因编程 Dupuis等[43], [115]
    18 多目标进化算法 Fan等[10], Wen和Xu [57], Farnsworth[61], Jamwal等[118]
    19 基于替代模型辅助的进化算法 Liu等[39], [63-64], Akinsolu等[52], Wang等[117]
    下载: 导出CSV
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  • 收稿日期:  2019-11-04
  • 录用日期:  2020-03-25

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