基于多目标人工蜂鸟算法的研制保证等级分配

doi:10.7527/S1000-6893.2024.30946

Abstract

Abstract:

Development Assurance Level （DAL） assignment is an important task in the process of aircraft system development. It is usually required to minimize development costs on the basis of satisfying the DAL assignment principles. A multi-objective optimization model for DAL assignment is constructed. The DAL assignment principles and the top-level failure condition probability requirement are taken as qualitative and quantitative constraints， respectively. The optimization objectives are the minimization of development cost and system weight. The DALs assigned to functions or items in the system are taken as decision variables. Besides， a method for DAL assignment is proposed based on the multi-objective artificial hummingbird algorithm. A case study is given based on a fly-by-wire system， and a non-dominated solution set for DAL assignment is obtained. Under the same test condition， the proposed method reduces the execution time by 17.59% and improves the HV metric by 63.49% compared with multi-objective particle swarm optimization， which indicates that the proposed method can converge quickly and the obtained solution set has good distribution.

Key words: system safety, Development Assurance Level (DAL) assignment, multi-objective optimization, multi-objective artificial hummingbird algorithm, failure probability

CLC Number:

V328.5

Yuxin CUI, Zhong LU, Jia ZHOU. Development assurance level assignment based on multi-objective artificial hummingbird algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(4): 330946.

Figures/Tables 15

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References 33

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影响等级	DAL	概率要求
灾难性的	A	10^-9
危害性的	B	10^-7
严重的	C	10^-5
轻微的	D	10^-3

F/I序号	重量/kg				F/I序号	重量/kg
F/I序号	A	B	C	D	F/I序号	A	B	C	D
1	4.8	5.5	4.6	4.0	7	3.4	3.9	3.6	4.5
2	3.5	4.6	4.4	3.1	8	1.9	2.2	1.8	2.3
3	3.5	3.2	4.3	3.3	9	3.3	3.5	2.4	2.8
4	4.7	4.9	4.0	4.4	10	1.5	1.3	1.5	1.9
5	5.2	5.7	5.4	5.8	11	5.8	5.5	5.3	5.6
6	3.6	2.9	2.7	3.2	12	2.1	2.0	2.4	1.8

No	F/I序号												费用/（10³美元）	质量/kg
No	1	2	3	4	5	6	7	8	9	10	11	12	费用/（10³美元）	质量/kg
1	C	C	A	C	C	C	C	C	A	C	C	C	585	42.5
2	B	A	B	C	C	C	C	A	C	C	C	C	601	41.4
3	C	A	B	C	C	B	C	A	C	C	C	C	614	40.7
4	A	A	B	C	C	C	C	A	C	C	C	C	614	40.7
5	C	A	B	C	C	B	C	A	C	B	C	C	632	40.5
6	A	A	B	C	C	C	C	A	C	B	C	C	632	40.5
7	C	A	B	C	C	B	C	A	C	C	C	B	635	40.3
8	A	A	B	C	C	C	C	A	C	C	C	B	635	40.3
9	C	A	B	C	C	B	C	A	C	B	C	B	653	40.1
10	A	A	B	C	C	C	C	A	C	B	C	B	653	40.1
11	A	A	B	C	A	C	C	C	C	C	C	B	666	40.0
12	A	A	B	C	A	C	C	C	C	B	C	B	684	39.8
13	A	A	B	C	A	C	A	C	C	B	C	B	722	39.6

顶层FC	顶层FC影响等级	顶层功能的FDAL	FDAL的数值表示
丧失俯仰功能	灾难性的	A	4
丧失滚转功能	灾难性的	A	4
丧失偏航功能	灾难性的	A	4
高升力控制系统失效	危害性的	B	3
丧失减速功能	危害性的	B	3

F/I序号	质量/kg				F/I序号	质量/kg				F/I序号	质量/kg				F/I序号	质量/kg
F/I序号	A	B	C	D	F/I序号	A	B	C	D	F/I序号	A	B	C	D	F/I序号	A	B	C	D
1	0.7	0.6	0.5	0.5	13	12.5	12.0	11.6	11.9	25	0.6	0.6	0.5	0.5	37	6.0	6.6	6.5	6.0
2	0.7	0.6	0.4	0.5	14	0.7	0.8	0.8	0.7	26	0.1	0.1	0.1	0.1	38	18.4	19.0	18.7	18.6
3	0.7	0.5	0.6	0.5	15	12.6	12.8	11.9	11.8	27	0.1	0.1	0.1	0.1	39	17.5	17.6	16.3	15.0
4	0.5	0.6	0.5	0.4	16	0.7	0.9	0.8	0.7	28	0.8	0.7	0.6	0.6	40	12.3	13.8	12.9	12.1
5	0.7	0.7	0.6	0.5	17	12.0	12.2	11.0	11.7	29	1.3	1.4	1.2	1.0	41	8.5	8.3	8.2	8.1
6	0.6	0.5	0.5	0.4	18	0.8	0.7	0.8	0.7	30	2.3	2.2	2.0	2.0	42	0.6	0.7	0.5	0.4
7	12.6	11.9	12.4	11.4	19	0.1				31	2.2	2.3	1.9	1.8	43	12.3	12.0	11.9	11.7
8	0.8	0.9	0.9	0.8	20	0.1				32	2.1	2.0	2.1	1.9	44	0.9	0.8	0.8	0.8
9	12.6	11.9	12.4	11.4	21	3				33	6.5	6.2	6.0	6.2	45	0.8	0.9	0.8	0.7
10	0.8	0.8	0.9	0.7	22	12.8	11.9	12.3	11.7	34	7.3	7.3	7.0	6.9	46	0.8	0.9	0.9	0.7
11	0.3				23	0.8	0.9	0.7	0.7	35	6.9	6.3	6.5	6.7
12	0.7	0.6	0.5	0.5	24	0.4	0.4	0.3	0.4	36	7.0	6.9	6.8	6.8

Development assurance level assignment based on multi-objective artificial hummingbird algorithm

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F/I序号	DAL	F/I序号	DAL	F/I序号	DAL	F/I序号	DAL
1	B	13	B	25	B	37	A
2	B	14	C	26	C	38	B
3	C	15	A	27	C	39	A
4	C	16	B	28	B	40	A
5	C	17	A	29	A	41	A
6	C	18	C	30	B	42	D
7	A	19	A	31	A	43	A
8	A	20	A	32	A	44	D
9	A	21	A	33	B	45	D
10	C	22	A	34	A	46	D
11	A	23	B	35	C
12	C	24	C	36	C

算法	非支配解数量	SP	HV	运行时间/s
MOAHA	49	0.024 0	0.041 2	128.44
NSGA-Ⅱ	24	0.052 9	0.021 1	115.04
MOEA/D	20	0.270 6	0.044 3	142.30
MOPSO	35	0.020 6	0.025 2	155.86

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算法	N	N_Arc	网格数	惯性权重	认知学习因子	社会学习因子	变异率
MOPSO	100	100	20	0.9	2	2	0.1
MOAHA	100	100

F/I序号	DAL	F/I序号	DAL	F/I序号	DAL	F/I序号	DAL
1	B	13	B	25	B	37	A
2	B	14	C	26	C	38	B
3	C	15	A	27	C	39	A
4	C	16	B	28	B	40	A
5	C	17	A	29	A	41	A
6	C	18	C	30	B	42	D
7	A	19	A	31	A	43	A
8	A	20	A	32	A	44	D
9	A	21	A	33	B	45	D
10	C	22	A	34	A	46	D
11	A	23	B	35	C
12	C	24	C	36	C

F/I序号	DAL	F/I序号	DAL	F/I序号	DAL	F/I序号	DAL
1	B	13	B	25	B	37	A
2	B	14	C	26	C	38	B
3	C	15	A	27	C	39	A
4	C	16	B	28	B	40	A
5	C	17	A	29	A	41	A
6	C	18	C	30	B	42	D
7	A	19	A	31	A	43	A
8	A	20	A	32	A	44	D
9	A	21	A	33	B	45	D
10	C	22	A	34	A	46	D
11	A	23	B	35	C
12	C	24	C	36	C