基于改进BBO算法和模糊期望效果的反导武器目标分配建模与实现

doi:10.12305/j.issn.1001-506X.2023.11.21

Abstract

Abstract:

To solve the problem of weapon target allocation (WTA) of existing anti-missile weapons, which results in target misclassification and omission due to ignoring the uncertain characteristics of firing advantage and target intention value, the fuzzy expectation theory is introduced and the maximum cost-effectiveness ratio anti-missile WTA model is built based on the fuzzy expectation effect. According to the characteristics of the model, improved biogeography-based optimization (IBBO) is proposed Based on integer matrix coding.In this algorithm, the migration operation is improved through cosine dynamic adaptive strategy, and the interaction idea in the symbiotic organisms search (SOS) algorithm is introduced to design the mutation operation based on the symbiotic strategy.In addition, the IBBO algorithm is combined with fuzzy simulation to form a hybrid intelligent algorithm to solve the model. The simulation results show that the proposed algorithm harmonizes the intensification and diversification capabilities, improves the accuracy and efficiency of the solution, and adapts to the requirements of the solution accuracy and timeliness for the anti-missile assisted decision in the uncertain environment.

Key words: fuzzy expectation, anti-missile weapon target allocation (WTA), improved biogeography-based optimization (IBBO) algorithm, cosine dynamic adaptive strategy, symbiosis strategy

CLC Number:

TP182

Xiaowen ZHU, Chengli FAN, Yingqi LU, Wenzheng ZHU, Xuan WU. Anti-missile weapon target allocation modeling and implementation based on improved BBO algorithm and fuzzy expectation effect[J]. Systems Engineering and Electronics, 2023, 45(11): 3544-3554.

Figures/Tables 17

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射击有利度	武器平台1	武器平台2	武器平台3	武器平台4
目标1	(0.8, 0.82, 0.84)	(0.36, 0.4, 0.44)	(0.56, 0.58, 0.6)	(0.87, 0.9, 0.93)
目标2	(0.6, 0.62, 0.64)	(0, 0, 0)	(0, 0, 0)	(0.62, 0.66, 0.7)
目标3	(0.9, 0.92, 0.94)	(0.58, 0.6, 0.62)	(0.46, 0.5, 0.54)	(0.68, 0.72, 0.76)
目标4	(0, 0, 0)	(0.76, 0.78, 0.8)	(0, 0, 0)	(0.87, 0.91, 0.95)
目标5	(0.51, 0.52, 0.53)	(0, 0, 0)	(0, 0, 0)	(0.56, 0.57, 0.58)
目标6	(0.76, 0.79, 0.82)	(0.48, 0.5, 0.52)	(0.46, 0.48, 0.5)	(0.61, 0.63, 0.65)
目标7	(0, 0, 0)	(0.58, 0.6, 0.62)	(0.7, 0.73, 0.76)	(0.68, 0.7, 0.72)
目标8	(0.48, 0.5, 0.52)	(0, 0, 0)	(0.5, 0.52, 0.54)	(0, 0, 0)
目标9	(0.54, 0.58, 0.62)	(0.51, 0.55, 0.59)	(0.9, 0.92, 0.94)	(0.66, 0.7, 0.74)
目标10	(0.76, 0.8, 0.84)	(0, 0, 0)	(0.53, 0.6, 0.67)	(0, 0, 0)

目标	目标意图价值	目标	目标意图价值
目标1	(0.76, 0.8, 0.84)	目标6	(0.88, 0.9, 0.92)
目标2	(0.86, 0.88, 0.9)	目标7	(0.47, 0.5, 0.53)
目标3	(0.9, 0.92, 0.94)	目标8	(0.67, 0.7, 0.73)
目标4	(0.89, 0.91, 0.93)	目标9	(0.86, 0.89, 0.92)
目标5	(0.7, 0.72, 0.74)	目标10	(0.87, 0.9, 0.93)

M_max	M_min	均值	排序
1	0	7.63E-01	1
0.9	0.1	7.63E-01	2
0.8	0.2	7.53E-01	5
0.7	0.3	7.57E-01	3
0.6	0.4	7.44E-01	4

θ取值	(1-θ)取值	均值	排序
0.1	0.9	7.08E-01	9
0.2	0.8	7.22E-01	8
0.3	0.7	7.13E-01	7
0.4	0.6	7.59E-01	6
0.5	0.5	7.64E-01	5
0.6	0.4	7.97E-01	4
0.7	0.3	8.03E-01	3
0.8	0.2	8.07E-01	2
0.9	0.1	8.12E-01	1

武器平台	目标1	目标2	目标3	目标4	目标5
1	1	1	16	1	1
2	1	1	3	12	1
3	1	1	5	1	1
4	16	1	1	1	1
武器平台	目标6	目标7	目标8	目标9	目标10
1	1	1	1	1	1
2	1	1	1	3	1
3	1	1	1	12	1
4	1	1	1	1	1

Anti-missile weapon target allocation modeling and implementation based on improved BBO algorithm and fuzzy expectation effect

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比较指标		BBO	BBODE	BBOPSODE	NSBBO	DGBBO	本文算法
全局解	最优解	7.40E-01	7.61E-01	7.56E-01	7.57E-01	7.65E-01	7.81E-01
	最差解	6.97E-01	7.12E-01	7.04E-01	7.15E-01	7.25E-01	7.50E-01
	平均解	7.19E-01	7.36E-01	7.29E-01	7.35E-01	7.43E-01	7.66E-01
	标准差	1.23E-02	1.44E-02	1.55E-02	1.24E-02	1.16E-02	9.00E-03
平均运行时间/s		15.45	23.631	28.345	18.342	20.039	16.173
首次出现最优解的代数		48	68	45	52	80	62

迭代次数	种群规模
	50			100			200
	最优值	平均值	运行时间/s	最优值	平均值	运行时间/s	最优值	平均值	运行时间/s
50	7.60E-01	7.42E-01	1.71E+00	7.67E-01	7.55E-01	3.58E+00	7.84E-01	7.61E-01	5.83E+00
100	7.71E-01	7.50E-01	3.09E+00	7.77E-01	7.62E-01	8.31E+00	7.91E-01	7.65E-01	1.61E+01
200	7.79E-01	7.50E-01	7.59E+00	7.81E-01	7.66E-01	1.62E+01	7.92E-01	7.70E-01	2.51E+01

算法	规模	运行时间/s
BBO	4×4	14.051
	10×10	21.987
	50×50	30.980
BBODE	4×4	23.631
	10×10	28.023
	50×50	38.292
本文算法	4×4	16.173
	10×10	19.933
	50×50	29.098
BBOPSODE	4×4	18.012
	10×10	28.353
	50×50	39.134
NSBBO	4×4	16.352
	10×10	21.523
	50×50	38.242
DGBBO	4×4	16.671
	10×10	19.123
	50×50	29.123

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