Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study

被引:21
作者
Cao, Ming [1 ]
Fang, Weiguo [1 ,2 ]
机构
[1] Beihang Univ, Sch Econ & Management, Beijing 100083, Peoples R China
[2] Beihang Univ, Key Lab Complex Syst Anal Management & Decis, Minist Educ, Beijing 100083, Peoples R China
来源
SYMMETRY-BASEL | 2020年 / 12卷 / 05期
基金
中国国家自然科学基金;
关键词
weapon-target assignment; heuristic algorithms; particle swarm optimization; ant colony optimization; sine cosine algorithm; swarm intelligence; ALLOCATION; COLONY; OPTIMIZATION; SYSTEM;
D O I
10.3390/sym12050824
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy's attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for practical large-scale WTA problems. The effectiveness and performance of various algorithms are evaluated and compared by means of a benchmark problem with a small scale, the theoretical optimal solution of which is known. The four algorithms can obtain satisfactory solutions to the benchmark problem with high quality and high robustness, while IPSO is superior to BPSO, ACO and SCA with respect to the solution quality, algorithmic robustness and computational efficiency. Then, IPSO is applied to a large-scale WTA problem, and its effectiveness and performance are further assessed. We demonstrate that IPSO is capable of solving large-scale WTA problems with high efficiency, high quality and high robustness, thus meeting the critical requirements of real-time decision-making in modern warfare.
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收藏
页数:20
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