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

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

范衠^{1, 2,},
朱贵杰^{1, 2,},
李文姬^{1, 2,},
游煜根^{1, 2,},
李晓明^{1, 2,},
林培涵^{1, 2,},
辛斌^3,

1.
汕头大学工学院汕头 515063
2.
广东省数字信号与图像处理技术重点实验室汕头 515063
3.
北京理工大学自动化学院北京 100081

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), 广东省教委科研项目(2017KZDXM032), 广东省国际科技合作平台项目(2019A050519008)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.E-mail: 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. 主要研究方向为智能机器人系统的分析与设计.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

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出版历程
- 收稿日期: 2019-11-04
- 录用日期: 2020-03-25
- 网络出版日期: 2021-07-27
- 刊出日期: 2021-07-27

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

FAN Zhun^{1, 2
,},
ZHU Gui-Jie^{1, 2
,},
LI Wen-Ji^{1, 2
,},
YOU Yu-Gen^{1, 2
,},
LI Xiao-Ming^{1, 2
,},
LIN Pei-Han^{1, 2
,},
XIN Bin^3
,

1.
College of Engineering, Shantou University, Shantou 515063
2.
Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou 515063
3.
School of Automation, Beijing Institute of Technology, Beijing 100081

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 (2019A050519008)

More Information

Author Bio:
FAN Zhun　Ph.D., professor at the College of Engineering, Shantou University, China. He received his Ph. D. degree from electrical engineering, Michigan State University, USA, in 2004. His research interest covers mechatronics design automation, robotics, and evolutionary computation

ZHU Gui-Jie　Ph.D. candidate at the College of Engineering, Shantou University. His research interest covers mechatronics design automation and intelligent robot system

LI Wen-Ji　Ph.D. candidate at the College of Engineering, Shantou University. His main research interest is constrained multi-objective evolutionary computation

YOU Yu-Gen　He received his master degree from the College of Engineering, Shantou University in 2019. His research interest covers multi-objective evolutionary optimization, optimal design and control of robot

LI Xiao-Ming　Master student at the College of Engineering, Shantou University. His main research interest is optimization control of intelligent robot system

LIN Pei-Han　Master student at the College of Engineering, Shantou University. His research interest covers analysis and design of intelligent robot system

XIN Bin　Ph.D., professor at the School of Automation, Beijing Institute of Technology. He received his Ph. D. degree from School of Automation, Beijing Institute of Technology in 2012. His research interest covers computational intelligence and multi-robot system. Corresponding author of this paper

摘要

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.
- 进化计算 /
- 复杂机电系统 /
- 设计自动化 /
- 知识型工作自动化 /
- 基因编程
Abstract:
The design automation of complex mechatronic system is an important branch of knowledge automation, which has great theoretical significance and practical value in robot system, high-end computerized numerical control machine and intelligent equipment system design. This paper gives a review of applications of evolutionary algorithms in the design automation of complex mechatronic system. First, some basic algorithms in the field of evolutionary computation and their advantages are briefly introduced. Next, a comprehensive summary of applications of evolutionary algorithms in the design automation of electronic systems, micro-electro-mechanical systems, and complex mechatronic system are presented, respectively. Then, we select a typic complex mechatronic system, i.e., a robot system, to discuss the development of robot design automation with evolutionary algorithms. Finally, some common key issues of applications of evolutionary algorithms in the design automation of complex mechatronic system have been discussed and prospected.
- Evolutionary computation /
- complex mechatronic system /
- design automation /
- automation of knowledge work /
- genetic programming

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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

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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

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图 3 进化计算的基本流程框架

Fig. 3 Basic flow framework of evolutionary computation

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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

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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

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图 6 基因型-表现型的映射实例

Fig. 6 An instance of genotype-phenotype mapping

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图 7 机电系统的进化设计框架

Fig. 7 The framework of evolutionary synthesis of mechatronic systems

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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]

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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
特性	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	√			√	√

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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], 解光军等^[44], Nabavi等^[54-56], Li等^[83], Yousfi等^[89], 陈启鹏等^[95]
3	进化策略算法	Kim^[34], Mallick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^[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等^[36]
10	键合图+遗传算法	Tay等^[78]
11	遗传算法+粒子群算法	Lapa等^[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等 ^[57], Farnsworth等^[61], Jamwal等^[117]
19	基于替代模型辅助的进化算法	Liu等^{[39, 63-64]}, Akinsolu等^[52], Wang等^[118]

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