Abstract
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.
Highlights
Dynamic command, control and battle management functions require fast and effective decision aids to provide the optimal allocation of resources for effective engagement and real-time battle damage assessment
We adopt three kinds of heuristic algorithms inspired by swarm intelligence, including particle swarm optimization (PSO), ant colony optimization (ACO) and sine cosine algorithm (SCA), to solve the multi-constraint weapon-target assignment (WTA) problem in the context of multilayer defense, wherein we develop two discrete versions of PSO, i.e., integer PSO (IPSO) and binary PSO
We demonstrate that integer particle swarm optimization (IPSO) is capable of solving large-scale WTA problems with very high efficiency and high quality, meeting the critical requirements of real-time decision-making in modern warfare
Summary
Control and battle management functions require fast and effective decision aids to provide the optimal allocation of resources for effective engagement and real-time battle damage assessment. Studies on WTA began in the 1950s, when the WTA modeling issues were originally confronted [1,2]. In those days, due to limited computing power, scholars were forced to make a set of simplification assumptions in modeling and could only tackle some small-scale WTA problems by using classic discrete optimization methods, such as the dynamic programming or branch and bound method. In the past 30 years, many scholars and practitioners have continuously extended and improved WTA models and solving algorithms, including extending from single-layer to multilayer defense scenarios; from static allocation to dynamic allocation situations; from considering only the availability of weapons to considering more constraints, including manpower, budget and space
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