A Suite of Metaheuristic Algorithms for Static Weapon-Target Allocation

The problem of allocating defensive weapon resources to hostile targets is an optimization problem of high military relevance. The need for obtaining the solutions in real-time is often overlooked in existing literature. Moreover, there does not exist much research aimed at comparing the performance of different algorithms for weapon-target allocation. We have implemented a suite of metaheuristic algorithms for solving the static weapon-target allocation problem, and compare their real-time performance on a large set of problem instances using the open source testbed SWARD. The compared metaheuristic algorithms are ant colony optimization, genetic algorithms, and particle swarm optimization. Additionally, we have compared the quality of the generated allocations to those generated by a well-known maximum marginal return algorithm. The results show that the metaheuristic algorithms perform well on small- and medium-scale problem sizes, but that real-time requirements limit their usefulness for large search spaces.

[1]  Zne-Jung Lee,et al.  Parallel Ant Colonies with Heuristics Applied to Weapon-Target Assignment Problems , 2002 .

[2]  Chou-Yuan Lee,et al.  An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem , 2002, Appl. Soft Comput..

[3]  Fredrik Johansson,et al.  A Bayesian network approach to threat evaluation with application to an air defense scenario , 2008, 2008 11th International Conference on Information Fusion.

[4]  G. G. denBroeder,et al.  On Optimum Target Assignments , 1959 .

[5]  Krishna C. Jha,et al.  Exact and Heuristic Methods for the Weapon Target Assignment Problem , 2003 .

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[8]  Michel Gendreau,et al.  Metaheuristics in Combinatorial Optimization , 2022 .

[9]  Yingwu Chen,et al.  Survey of the research on dynamic weapon-target assignment problem , 2006 .

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Michael N. Vrahatis,et al.  Particle swarm optimization for integer programming , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[13]  Yunlong Zhu,et al.  Solving Weapon-Target Assignment Problem using Discrete Particle Swarm Optimization , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[14]  Patrick A. Hosein,et al.  A class of dynamic nonlinear resource allocation problems , 1989 .