UAV Cooperative Task Assignments for a SEAD Mission Using Genetic Algorithms

Darrah, Marjorie; Niland, William; Stolarik, Brian; Walp, Lance E.

doi:10.2514/6.2006-6456

Cited by 34 publications

(29 citation statements)

References 4 publications

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“…Thus the optimal scheme is 4 x , that is to say, the automation level of TC takes 4 the most appropriate.…”

Section: A Automation Level Range Of Tcmentioning

confidence: 99%

“…Step 1: The decision makers give out the uncertain linguistic assessment values of the influence degree ij r and ij q respectively, which are the capability advantages of man (4) Note that ) (x floor is a Gauss integral function.…”

Section: B Automation Level Range Of Decision Allocationmentioning

confidence: 99%

“…On the other hand, decision-making is partially optimized with the improvement of computing capability and the progress of decision-making algorithm, but there are still some questions, which may hard to solve or need long computation time. Heuristic methods can not always provide satisfying solutions for these problems [3,4,5,6] .…”

Section: Introductionmentioning

confidence: 99%

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Decision Allocation of Formation Command and Control System

Liu¹,

Chen²,

Fan-e³

2013

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics

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In order to adequately exert the decision advantages of commander and automation, we need to do decision allocation between them during the design process of manned/unmanned aerial vehicle formation command and control system. The decision allocation process was proposed. For the property values are hard to accurately estimate, we use Uncertain Linguistic Multiple Attribute Decision Making (ULMADM) method to aggregate the property values. Uncertain extended weighted arithmetic averaging (UEWAA) operator is used to determine the range of automation level. A group decision making method combined with UEWAA operator and uncertain linguistic hybrid aggregation (ULHA) operator is used to determine the automation level. Simulation result indicates the method is feasible and effective in decision allocation.Keywords-manned/unmanned aerial vehicle formation, command and control system, decision allocation, levels of automation, Uncertain Linguistic Multiple Attribute Decision Making (ULMADM).

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“…Thus the optimal scheme is 4 x , that is to say, the automation level of TC takes 4 the most appropriate.…”

Section: A Automation Level Range Of Tcmentioning

confidence: 99%

Section: B Automation Level Range Of Decision Allocationmentioning

confidence: 99%

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Decision Allocation of Formation Command and Control System

Liu¹,

Chen²,

Fan-e³

2013

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics

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“…It mainly includes the MAMs based on market mechanism, dynamic network flow optimization, multi-agent sufficing decision making, mixed integer linear programming, modern intelligent optimization algorithms, and synthesized evolution algorithms with the method of multi-objective integer programming [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A new approach to solve the mission assignment problem for cooperative UCAVs using immune particle swarm optimizations

Wang

Deng

et al. 2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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This paper first builds a mathematical model for the mission assignment problem (MAP) of cooperative multiple uninhabited combat aerial vehicles (UCAVs). To address challenges posed by the specific MAP problem, we propose a new immune particle swarm optimization (NIPSO) approach through incorporating immunity memory, diversity clone, and immune selection in artificial immune algorithm into standard PSO. The simulation results achieved on a typical scenario show that our NIPSO approach for multiple UCAVs is capable of substantially speeding up convergence and has stronger ability to find the global optimum than that of classical PSO. The MAP solution for cooperative UCAVs is significantly improved.Index Terms -Uninhabited Combat Aerial Vehicles (UCAV); mission assignment problem; artificial immune algorithm; Particle Swarm Optimization (PSO).

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“…However, when the mission involves more than three vehicles and two targets, the GA provided a feasible solution in a greatly reduced time. Our work studied the GA approach, formulation, implementation, and integration into a high fidelity simulation [20].…”

Section: Algorithm Researchmentioning

confidence: 99%

Intelligent Flight Control Simulation Research Program

Stolarik¹

2007

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UAV Cooperative Task Assignments for a SEAD Mission Using Genetic Algorithms

Cited by 34 publications

References 4 publications

Decision Allocation of Formation Command and Control System

Decision Allocation of Formation Command and Control System

A new approach to solve the mission assignment problem for cooperative UCAVs using immune particle swarm optimizations

Intelligent Flight Control Simulation Research Program

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