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

摘要/Abstract

摘要：

针对现有反导武器目标分配(weapon target allocation, WTA)由于忽略射击有利度和目标意图价值的不确定特征, 而造成目标错分、漏分的问题, 引入模糊期望理论, 构建基于模糊期望效果的最大化费效比反导WTA模型。针对模型特点, 提出基于改进型生物地理优化(improved biogeography-based optimization, IBBO)算法。该算法采用基于整数的矩阵编码方式, 通过余弦动态自适应策略改进迁移操作。同时, 引入共生生物搜索(symbiotic organisms search, SOS)算法中相互作用思想, 设计基于共生策略的变异操作。此外, 在IBBO算法基础上结合模糊模拟形成混合智能算法, 对模型进行求解。仿真实例表明, 所提算法较好地协调了集约化和多样化的能力, 提升了求解的精度与效率, 满足不确定环境下反导辅助决策对求解精度和时效性的要求。

关键词: 模糊期望, 反导武器目标分配, 基于改进型生物地理优化算法, 余弦动态自适应策略, 共生策略

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

中图分类号:

TP182

朱晓雯, 范成礼, 卢盈齐, 朱文正, 吴暄. 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[J]. 系统工程与电子技术, 2023, 45(11): 3544-3554.

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.

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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