任务主体二元约束下作战任务分解EVA方法

doi:10.12305/j.issn.1001-506X.2022.07.16

Abstract

Abstract:

Aiming at the arbitrariness and uncertainty in the decomposition of complex combat tasks, a extraction-verification-adjustment (EVA) task decomposition method is proposed, which is formed by the extraction, verification, and adjustment of the sub task set, considering the binary constraints such as the capability attributes and structural characteristics of the task subject. First, the global task space is constructed, and the subtask set extraction method based on task matching is proposed. Secondly, according to the binary constraints of the task subject's ability attributes and structural characteristics, a quantitative adjustment model of subtask sets is established. By improving the elitist retention strategy and introducing task decomposition granularity and the dynamic adjustment strategy of crossover mutation probability, the improved non-dominated sorting genetic algorithm-Ⅱ (INSGA-Ⅱ) algorithm is proposed. Finally, the simulation results verify that INSGA-Ⅱ has the advantages of diversity, convergence in solution set and timeliness performance compared with the traditional optimization algorithms. The results show that the method proposed in this paper enable decision makers to adjust and control the task decomposition results according to the actual situation of the task subject, and overcome the problem that the traditional methods rely on subjective experience and ignore the constraints of the ability attributes and structural characteristics of the task subject.

Key words: combat task decomposition, task subject, binary constraints, extraction-verification-adjustment (EVA) method, multi-objective optimization, task descomposition

CLC Number:

E917

Qian LIU, Yunjun LU, Kebin CHEN, Mengyao HAN, Liang GUO. Combat task decomposition EVA method based on binary constraints of task subject[J]. Systems Engineering and Electronics, 2022, 44(7): 2201-2210.

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染色体编码	子任务编号	染色体编码	子任务编号
T₁	1	T₅	3
T₂	1	T₆	3
T₃	2	T₇	2
T₄	1	T₈	1

任务T	T₁	T₂	T₃	T₄	T₅	T₆	T₇	T₈	T₉	T₁₀	T₁₁	T₁₂	T₁₃
T₁	0	0.233 1	0	0.423 8	0	0	0	0	0	0	0	0	0
T₂	0.233 1	0	0.287 7	0	0	0	0	0	0	0	0	0	0
T₃	0	0.287 7	0	0	0.611 2	0.643 6	0	0	0	0	0	0	0
T₄	0.423 8	0	0	0	0	0	0	0	0	0.701 7	0.685 4	0	0
T₅	0	0	0.611 2	0	0	0	0.621 6	0	0	0	0	0	0
T₆	0	0	0.643 6	0	0	0	0.725 9	0	0	0	0	0	0
T₇	0	0	0	0	0.621 6	0.725 9	0	0.340 9	0	0	0	0	0
T₈	0	0	0	0	0	0	0.340 9	0	0.823 3	0	0	0	0
T₉	0	0	0	0	0	0	0	0.823 3	0	0	0	0	0
T₁₀	0	0	0	0.701 7	0	0	0	0	0	0	0	0.428 5	0
T₁₁	0	0	0	0.685 4	0	0	0	0	0	0	0	0.470 2	0
T₁₂	0	0	0	0	0	0	0	0	0	0.428 5	0.470 2	0	0.852 8
T₁₃	0	0	0	0	0	0	0	0	0	0	0	0.852 8	0

任务T	T₁₄	T₁₅	T₁₆	T₁₇	T₁₈	T₁₉
T₁₄	0	0.110 3	0.049 3	0.955 3	0.048 7	0
T₁₅	0.110 3	0	0.593 0	0.237 0	1	0.548 6
T₁₆	0.049 3	0.593 0	0	0.160 8	0.593 9	0.875 7
T₁₇	0.955 3	0.237 0	0.160 8	0	0.160 2	0.101 4
T₁₈	0.048 7	1	0.593 9	0.160 2	0	0.601 4
T₁₉	0	0.548 6	0.875 7	0.101 4	0.601 4	0

能力C	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉	C₁₀
T₁	0	0	0	0	0	0	0	10	0	0
T₂	0	0	0	10	13	10	6	0	0	0
T₃	0	0	0	11	15	13	0	0	0	0
T₄ T₁₀	0	0	0	20	14	12	0	0	0	0
T₅	0	1	0	0	0	0	0	5	0	0
T₆ T₇	0	0	0	8	6	10	0	4	0	10
T₈ T₉	0	0	0	0	0	8	0	0	10	6
T₁₁	0	0	0	0	0	0	0	5	0	0
T₁₂ T₁₃	0	0	0	0	0	10	0	0	0	6
T₁₄ T₁₇	10	0	0	0	0	0	0	0	0	0
T₁₅ T₁₈	0	6	0	0	0	0	0	0	0	0
T₁₆ T₁₉	0	0	20	0	0	0	0	0	0	0

量化调整结果	SD_inner	SD_inter
{1, 2, 2}	0.769 8	0.205 3

Combat task decomposition EVA method based on binary constraints of task subject

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主体S	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉	C₁₀
S₁	0	5	0	25	16	14	8	12	0	12
S₂	12	0	10	9	0	0	10	0	5	0
S₃	14	10	0	9	10	14	0	6	0	7
S₄	15	9	0	0	0	11	8	0	15	10
S₅	0	6	0	12	15	15	10	12	12	12
S₆	0	9	23	0	8	0	0	0	0	0

序号	量化调整结果	MD_inner	MD_inter
1	{1, 2, 3, 1, 3, 3, 4, 1, 1}	0.373 6	0.015 4
2	{1, 1, 2, 3, 2, 2, 4, 3, 3}	0.451 0	0.017 5
3	{1, 1, 2, 1, 2, 2, 3, 1, 4}	0.529 1	0.024 3
4	{1, 2, 2, 1, 2, 2, 3, 1, 4}	0.566 7	0.024 5
5	{1, 1, 2, 1, 2, 2, 3, 1, 4}	0.578 6	0.025 5
6	{1, 1, 1, 2, 1, 1, 3, 2, 4}	0.586 7	0.028 7
7	{1, 1, 1, 2, 1, 1, 3, 1, 4}	0.623 9	0.046 5
8	{1, 1, 1, 1, 1, 2, 3, 1, 4}	0.628 6	0.050 2

优化目标		NSGA-Ⅱ	MOEA/D	INSGA-Ⅱ
第1组	MD_inner	0.520 3	0.557 9	0.542 3
第1组	MD_inter	0.032 7	0.031 5	0.029 1
第2组	MD_inner	0.499 1	0.558 2	0.545 8
第2组	MD_inter	0.029 2	0.034 6	0.024 1
第3组	MD_inner	0.486 6	0.530 9	0.558 2
第3组	MD_inter	0.022 8	0.024 9	0.034 6

算法		第1组	第2组	第3组
NSGA-Ⅱ	MOEA/D	0.29	0.38	0.44
NSGA-Ⅱ	INSGA-Ⅱ	0.14	0.29	0.25
MOEA/D	NSGA-Ⅱ	1.00	1.00	1.00
MOEA/D	INSGA-Ⅱ	0.57	0.71	0.75
INSGA-Ⅱ	NSGA-Ⅱ	1.00	1.00	1.00
INSGA-Ⅱ	MOEA/D	1.00	1.00	1.00

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