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Open AccessArticle

A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem

1
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2
First Military Representative Office of Air Force Equipment Department, People’s Liberation Army Air Force, Chengdu 610013, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3803; https://doi.org/10.3390/app9183803
Received: 31 July 2019 / Revised: 29 August 2019 / Accepted: 7 September 2019 / Published: 11 September 2019
(This article belongs to the Section Computing and Artificial Intelligence)
The sensor-weapon–target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks. View Full-Text
Keywords: cooperative engagement; sensor-weapon–target assignment (S-WTA); evolutionary algorithm (EA); genetic algorithm (GA) cooperative engagement; sensor-weapon–target assignment (S-WTA); evolutionary algorithm (EA); genetic algorithm (GA)
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Li, X.; Zhou, D.; Yang, Z.; Pan, Q.; Huang, J. A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem. Appl. Sci. 2019, 9, 3803.

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