An anytime algorithm based on decentralized cooperative auction for dynamic joint fire distribution problem

Zi-fen, Li; Li, Xiangmin; Ji, Yan; Dai, Jinjin; Fan-e, Kong

doi:10.1109/icma.2012.6285134

Cited by 5 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, sensor assignment will affect the benefit of the assignment of weapons. Li et al [22] and Chen et al [24] present a similar dependent S-WTA model to model (2), but reformulate the problem with a different method of calculating the benefits. The model is shown as…”

Section: Framework B: the Dependent S-wta Framework-imentioning

confidence: 99%

“…In order to improve the efficiency of the allocation schemes over many dynamic network topologies, Li et al [21] proposed an improved Swt-opt, combining the consensus algorithm with Swt-opt. Li et al [22] introduced the temporal and spatial constraints into the S-WTA problem to establish an improved dependent model. They considered this specific S-WTA problem as a kind of dynamic weapon-target assignment (DWTA) [23] problem and proposed an anytime algorithm based on decentralized cooperative auction to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhou

Yang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Framework B: the Dependent S-wta Framework-imentioning

confidence: 99%