Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study
Proceedings of the 10th annual conference on Genetic and evolutionary computation
String- and permutation-coded genetic algorithms for the static weapon-target assignment problem
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A semi-automated dynamic approach to threat evaluation and optimal defensive resource allocation
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Employment of an evolutionary heuristic to solve the target allocation problem efficiently
Information Sciences: an International Journal
Approximating the optimal mapping for weapon target assignment by fuzzy reasoning
Information Sciences: an International Journal
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The weapon-target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. This problem consists of optimally assigning n weapons to m targets so that the total expected survival value of the targets after all the engagements is minimal. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. No exact methods exist for the WTA problem that can solve even small-size problems (for example, with 20 weapons and 20 targets). Although several heuristic methods have been proposed to solve the WTA problem, due to the absence of exact methods, no estimates are available on the quality of solutions produced by such heuristics. In this paper, we suggest integer programming and network flow-based lower-bounding methods that we obtain using a branch-and-bound algorithm for the WTA problem. We also propose a network flow-based construction heuristic and a very large-scale neighborhood (VLSN) search algorithm. We present computational results of our algorithms, which indicate that we can solve moderately large instances (up to 80 weapons and 80 targets) of the WTA problem optimally and obtain almost optimal solutions of fairly large instances (up to 200 weapons and 200 targets) within a few seconds.