Capability Construction of C4ISR Based on AI Planning

Jiao, Zhiqiang; Peiyang, Yao; Zhang, Jieyong; Wan, Lujun; Wang, Xun

doi:10.1109/access.2019.2902043

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…In order to realize the construction and evolution of C4ISR under the SOA framework, many experts and scholars have researched the capability requirements [3] [4], service encapsulation [5]- [7], service composition [8], [9], system evaluation and verification [10], [11], and other problems. This paper studies the deployment of services in the cloud environment; that is, it determines the deployment plan of the service based on the service process.…”

Section: Related Workmentioning

confidence: 99%

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

et al. 2020

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This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

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Section: Related Workmentioning

confidence: 99%

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

et al. 2020

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“…where R is the number of the sensor-weapon pair. In this paper, we assume that there are M sensors and W weapons, so R = M • W. The constraints of this formulation are the same as the model (1).…”

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

confidence: 99%

“…To analyze the efficiency of σ conveniently, we figured out the value of σ with x ik in [1,5]. x ik = 1 , then σ = 0 .…”

Section: ) Efficiency In the Proposed S-wta Modelmentioning

confidence: 99%

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

Zhou

Yang

et al. 2019

Applied Sciences

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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.

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“…As a type of group algorithm, the population of ABC algorithm exists in the form of a non-integer in the optimization process. The neighborhood search mechanism [28]- [30] aims to realize the mapping between the infeasible non-integer solution and a feasible integer solution.…”

Section: ) Neighborhood Search Mechanismmentioning

confidence: 99%

Holonic-C2 Organization Structure Generation Method Based on Clustering Optimization Algorithms

et al. 2019

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For Holonic-C2 organization-structure generation, a set of methods based on task-cluster clustering and platform-set optimization are proposed. Initially, the mathematical descriptions of the Holonic-C2 organization components are presented, and the characteristics and advantages of the Holonic-C2 organization are analyzed. To address the critical problems of task cluster and platform-set construction in the organization structure, with the equalization of the task resource requirements and platform resource capabilities, the mathematical models for two-stage clustering optimization are established, respectively. The multi-view clustering optimization and neighborhood search artificial bee colony algorithms are proposed and applied, respectively, for solving these mathematical models. Finally, Monte Carlo simulations are performed, and the obtained results demonstrate the effectiveness of the proposed models and algorithms.

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Capability Construction of C4ISR Based on AI Planning

Cited by 12 publications

References 20 publications

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

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

Holonic-C2 Organization Structure Generation Method Based on Clustering Optimization Algorithms

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