基于方向协同的可靠性冗余设计优化算法

doi:10.12305/j.issn.1001-506X.2025.06.17

摘要/Abstract

摘要：

可靠性冗余设计优化是提高系统可靠度的重要方法之一。在协同平衡优化算法的基础上提出基于方向协同的优化算法, 引入优化方向的概念, 更新寻优流程。所提算法在充分考虑冗余结构变化对系统整体的可靠度、成本、质量、体积与能耗等多方面参数影响的同时, 可显著减少计算量, 更高效地实现复杂系统冗余设计优化目标。研究表明, 基于方向协同的可靠性冗余设计优化算法适用于各类冗余设计优化任务, 同时具备结果准确、优化效果好、效率高等优点。

关键词: 可靠度, 冗余设计, 优化算法, 优化方向

Abstract:

Reliability redundancy design optimization is one of the important methods to improve system reliability. An optimization algorithm based on directional collaboration on the basis of collaborative balance optimization algorithm is proposed, the concept of optimization direction is introduced, and the optimization process is updated in this paper. The proposed algorithm fully considers the impact of changes in redundant structures on the overall reliability, cost, quality, volume and energy consumption of the system, while significantly reducing computational complexity and achieving the optimization goal of complex system redundancy design more efficiently. Research results show that the reliability redundancy design optimization algorithm based on directional collaboration is suitable for various redundancy design optimization tasks and has the advantages of accurate results, good optimization effects and high efficiency.

Key words: reliability, redundancy design, optimization algorithm, optimization direction

中图分类号:

TB114.3

韩冲, 段富海. 基于方向协同的可靠性冗余设计优化算法[J]. 系统工程与电子技术, 2025, 47(6): 1893-1898.

Chong HAN, Fuhai DUAN. Optimization algorithm for reliability redundancy design based on directional collaboration[J]. Systems Engineering and Electronics, 2025, 47(6): 1893-1898.

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参考文献 31

1	兰雪. GO-FLOW法在电静液作动器可靠性分析中的应用研究[D]. 大连: 大连理工大学, 2018.
	LAN X. Research on the application of GO-FLOW methodology in the reliability analysis of electro-hydrostatic actuator[D]. Dalian: Dalian University of Technology, 2018.
2	WANG H P , DUAN F H , MA J , et al. Research on redundancy design of a certain aircraft hydraulic system based on goal-oriented methodology[J]. International Journal of System Assurance Engineering and Management, 2022, 14 (1): 343- 352.
3	PEI D B , LIU X Y , TIAN X B , et al. Design and implementation of GNSS receiver based on SoC platform[J]. Journal of Physics: Conference Series, 2021, 1952 (4): 042133. doi: 10.1088/1742-6596/1952/4/042133
4	YEH W C , ZHU W , TAN S Y , et al. Novel general active reliability redundancy allocation problems and algorithm[J]. Reliability Engineering and System Safety, 2022, 218, 108167. doi: 10.1016/j.ress.2021.108167
5	AGGARWAL K K . Redundancy optimization in general systems[J]. IEEE Trans.on Reliability, 1976, 25 (5): 330- 332.
6	HABIB A , AKRAM M . Optimizing traveling salesman problem using TABU search metaheuristic algorithm with Pythagorean fuzzy uncertainty[J]. Granular Computing, 2024, 9 (1): 16. doi: 10.1007/s41066-023-00424-8
7	TIAN M N , LIU J H , YUE W , et al. A novel integrated heuristic optimizer using a water cycle algorithm and gravitational search algorithm for optimization problems[J]. Mathematics, 2023, 11 (8): 54- 80.
8	KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proc. of the IEEE International Conference on Neural Networks, 1995: 1942-1948.
9	ARASARATNAM I , HAYKIN S , ELLIOTT R J . Discrete-time nonlinear filtering algorithms using gauss-hermite quadrature[J]. Proceeding of the IEEE, 2007, 95 (5): 953- 977. doi: 10.1109/JPROC.2007.894705
10	DAN Y P , LI Z . Particle swarm optimization-based convolutional neural network for handwritten Chinese character recognition[J]. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2023, 27 (2): 165- 172. doi: 10.20965/jaciii.2023.p0165
11	HAO J , HUANG F Y , SHEN X J , et al. An adaptive stochastic resonance detection method with a knowledge-based improved artificial fish swarm algorithm[J]. Multimedia Tools and Applications, 2022, 81 (8): 11773- 11794. doi: 10.1007/s11042-022-12076-y
12	SUN B , NIU N N . Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on im proved multi-objective particle swarm optimization algorithm[J]. Ocean Engineering, 2024, 311 (2): 118944.
13	NIE X Z , JIAO Y X . Research on transmission control of airborne communication data link system based on artificial fish swarm algorithm[J]. Automatic Control and Computer Sciences, 2023, 57 (4): 327- 336. doi: 10.3103/S0146411623040077
14	翟宏州, 张华, 吴琳娜, 等. 基于协同平衡的可靠性冗余设计优化算法[J]. 系统工程与电子技术, 2023, 45 (12): 4084- 4089. doi: 10.12305/j.issn.1001-506X.2023.12.39
	ZHAI H Z , ZHANG H , WU L N , et al. Optimization algorithm for reliability redundancy design based on collaborative balance[J]. Systems Engineering and Electronics, 2023, 45 (12): 4084- 4089. doi: 10.12305/j.issn.1001-506X.2023.12.39
15	OUZINEB M , NOURELFATH M , GENDREAU M . An efficient heuristic for reliability design optimization problems[J]. Computers and Operations Research, 2009, 37 (2): 223- 235.
16	LI J , WANG G D , ZHOU H F . Redundancy allocation optimization for multi-state system with hierarchical performance requirements[J]. Proceedings of the Institution of Mechanical Engineers, 2023, 237 (6): 1031- 1047.
17	AGGARWAL K K , GUPTA J S . On minimizing the cost of reliable systems[J]. IEEE Trans.on Reliability, 1975, 24 (3): 205.
18	MISRA K B , LJUBOJEVIC M D . Optimal reliability design of a system-a n ew look[J]. IEEE Trans.on Reliability, 1973, 22 (5): 255- 258.
19	KOICHI I , GANDHI S L , HENLEY E J . Optimal reliability design of process systems[J]. IEEE Trans.on Reliability, 1974, 23 (1): 29- 33.
20	TILLMAN F A , HWANG C L , FAN L T . Optimal reliability of a complex system[J]. IEEE Trans.on Reliability, 1970, 19 (3): 95- 100.
21	FARTTA L , MONTANARI U G . Synthesis of availability network[J]. IEEE Trans.on Reliability, 1976, 25 (1): 81- 87.
22	MUHURI P K , NATH R . A novel evolutionary algorithmic solution approach for bilevel reliability-redundancy allocation problem[J]. Reliability Engineering and System Safety, 2019, 191, 106531. doi: 10.1016/j.ress.2019.106531
23	黎放, 朱承, 王威. 冗余系统费用模型及优化[J]. 系统工程与电子技术, 2000, 22 (9): 84- 89. doi: 10.3321/j.issn:1001-506X.2000.09.026
	LI F , ZHU C , WANG W . Cost models and optimization of redundant system[J]. Systems Engineering and Electronics, 2000, 22 (9): 84- 89. doi: 10.3321/j.issn:1001-506X.2000.09.026
24	蒂尔曼·弗兰克·A, 黄清莱. 系统可靠性最优化[M]. 北京: 国防工业出版社, 1988.
	TILLMAN F A , HUANG Q L . Optimization of systems reliability[M]. Beijing: National Defense Industry Press, 1988.
25	赵志草, 宋保维, 杜喜昭. 可靠性冗余优化设计算法研究[J]. 机械设计, 2014, 31 (4): 1-3, 10.
	ZHAO Z C , SONG B W , DU X Z . Research of algorithm for reliability redundancy optimization design[J]. Journal of Machine Design, 2014, 31 (4): 1-3, 10.
26	HIKITA M , NAKAGAWA Y , NAKASHIMA K , et al. Reliability optimization of systems by a surrogate-constraints algorithm[J]. IEEE Trans.on Reliability, 1992, 41 (3): 473- 480. doi: 10.1109/24.159825
27	WU P F , GAO L Q , ZOU D X , et al. An improved particle swarm optimization algorithm for reliability problems[J]. ISA Transactions, 2010, 50 (1): 71- 81.
28	ARDAKAN M A , HAMADANI A Z . Reliability-redundancy allocation problem with cold-standby redundancy strategy[J]. Simulation Modelling Practice and Theory, 2014, 42, 107- 118. doi: 10.1016/j.simpat.2013.12.013
29	CHEN T C . IAs based approach for reliability redundancy allocation problems[J]. Applied Mathematics and Computation, 2006, 182 (2): 1556- 1567. doi: 10.1016/j.amc.2006.05.044
30	许迎春. 面向卫星不确定性建模的多源信息贝叶斯融合方法研究[D]. 长沙: 国防科技大学, 2019.
	XU Y C. Research on multi-source information bayesian melding method for satellite uncertainty modelling[D]. Changsha: National University of Defense Technology, 2019.
31	CASTET J F , SALEH J H . Satellite and satellite subsystems reliability: statistical data analysis and modeling[J]. Reliability Engineering and System Safety, 2009, 94 (11): 1718- 1728. doi: 10.1016/j.ress.2009.05.004

i	r_i	v_i	c_i/元	m_i	V₀/mm³	C₀/元	M₀/g
1	0.80	1	7	7	110	175	200
2	0.85	2	7	8	110	175	200
3	0.90	3	5	8	110	175	200
4	0.65	4	9	6	110	175	200
5	0.75	2	4	9	110	175	200

算法	优化冗余数	R_s	C_S/元	V_s/mm³	M_S/g
粒子群算法	[2, 3, 2, 3, 3]	0.892 4	146.12	88	195.53
一阶邻域启发式遗传算法	[3, 2, 2, 3, 3]	0.904 5	146.12	83	170.95
粒子群优化与修正后最优化冗余分配启发式算法-GAG1联合算法	[3, 2, 2, 3, 3]	0.904 5	146.12	83	170.95
方向协同优化算法	[3, 3, 2, 3, 4]	0.933 1	162.81	107	197.11

i	10⁵α_i	β_i	m_iv_i²	m_i	V₀/mm³	C₀/元	M₀/g	T/h
1	2.500	1.5	2	3.5	720	525	500	1 000
2	1.450	1.5	4	4.0	720	525	500	1 000
3	0.541	1.5	5	4.0	720	525	500	1 000
4	0.541	1.5	8	3.5	720	525	500	1 000
5	2.100	1.5	4	4.5	720	525	500	1 000

算法	初始样本	优化冗余数	计算样本数
平衡协同	[1, 1, 1, 1, 1]	[6, 4, 6, 5, 7]	1 701
优化算法	[3, 4, 8, 7, 3]	[6, 4, 6, 5, 7]	1 215
方向协同	[1, 1, 1, 1, 1]	[6, 4, 6, 5, 7]	854
优化算法	[3, 4, 8, 7, 3]	[6, 4, 6, 5, 7]	730

节点	含义	节点	含义	节点	含义	根节点
S₁¹	卫星系统	S₂¹	姿态控制系统	S₃¹	陀螺仪	P₁
				S₃²	传感器	P₂
				S₃³	反作用飞轮	P₃
		S₂²	电源系统	S₃⁴	电池	P₄
				S₃⁵	配电系统	P₅
				S₃⁶	太阳能电池阵	P₆
		S₂³	推进系统	—	—	P₁₀
		S₂⁴	有效载荷	S₃⁷	放大器	P₇
				S₃⁸	计算器	P₈
				S₃⁹	变换器	P₉
		S₂⁵	结构与机构	—	—	P₁₁
		S₂⁶	遥测跟踪系统	—	—	P₁₂