Boolean Networks-Based Auction Algorithm for Task Assignment of Multiple UAVs

Sun, Xiaofeng; Qi, Naiming; Yao, Weiran

doi:10.1155/2015/425356

Cited by 4 publications

(2 citation statements)

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“…Flight Distance between UCAV i and T j ( 1, 2, 3, 4; j = 1, 2, 3, 4) Sensor 1 Based on the normal distribution interval number theory, we first present the target information in Table 1 as normal distribution interval numbers by Equation 24, as shown in Table 4. Then we compute the corresponding criteria value matrix S (i,k) for each UCAV using Equations (10) and (11).…”

Section: Sensors Target Valuementioning

confidence: 99%

“…They include the mixed integer linear programming (MILP) formulation [4][5][6], the branch and bound tree search algorithm [7,8], and the dynamic programming algorithm [9] among others. Most research to date on the application of heuristic (metaheuristic) algorithms have either extracted some specific search rules based on the properties of the problem to obtain optimal or suboptimal solutions rapidly or introduced some local search mechanism to the basic algorithm framework to improve the solution quality, such as tabu search algorithms [10], auction algorithms [11], genetic algorithms [12][13][14][15], ant colony algorithms [16], and particle swarm optimization [17,18]. The algorithms mentioned above-whether belonging to exact algorithms or heuristic (metaheuristic) algorithms-have demonstrated the ability to provide optimal or suboptimal solutions for task assignment problems of UAVs or UCAVs in various mission scenarios but may become difficult to apply when there are uncertain parameters related to mission scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Combining an Extended SMAA-2 Method with Integer Linear Programming for Task Assignment of Multi-UCAV under Multiple Uncertainties

Wang

Luo²,

et al. 2018

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Uncertainty should be taken into account when establishing multiobjective task assignment models for multiple unmanned combat aerial vehicles (UCAVs) due to errors in the target information acquired by sensors, implicit preferences of the commander for operational objectives, and partially known weights of sensors. In this paper, we extend the stochastic multicriteria acceptability analysis-2 (SMAA-2) method and combine it with integer linear programming to achieve multiobjective task assignment for multi-UCAV under multiple uncertainties. We first represent the uncertain target information as normal distribution interval numbers so that the values of criteria (operational objectives) concerned can be computed based on the weighted arithmetic averaging operator. Thus, we obtain multiple criteria value matrices for each UCAV. Then, we propose a novel aggregation method to generate the final criteria value matrix based on which the holistic acceptability indices are computed by the extended SMAA-2 method. On this basis, we convert the task assignment model with uncertain parameters into an integer linear programming model without uncertainty so as to implement task assignment using the integer linear programming method. Finally, we conduct a case study and demonstrate the feasibility of the proposed method in solving the multiobjective task assignment problem multi-UCAV under multiple uncertainties.

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Section: Sensors Target Valuementioning

confidence: 99%