Method of task allocation of tactical communication support based on improved particle swarm optimization algorithm

被引：0
|
作者：
Hua N. [1 ]
Zhao Y.-L. [1 ]
Yu Z.-H. [1 ]
机构：
[1] College of Information and Navigation, Air Force Engineering University, Xi'an
来源：
Kongzhi yu Juece/Control and Decision | 2018年 / 33卷 / 09期
关键词：
Allocation probability; PSO; Tactical communication support; Task allocation; Twice search;
D O I：
10.13195/j.kzyjc.2017.0537
中图分类号：
学科分类号：
摘要：
For solving the problem of tactical communication support, a task allocation model is established, and a twice search particle swarm optimization(TSPSO) algorithm based on gradient descent is proposed. There are four parts added into the TSPSO algorithm, including determination of the extremum trap, particle twices search, set forbidden area, particle elimination and generated. The TSPSO algorithm and other four improved algorithms are applied to the optimization problem of four typical test functions. The results show that, the convergence accuracy of the TSPSO algorithm is higher, the convergence speed is faster. In the solution of the task allocation model, the probability distribution of the support units to communication places is used to encode and decode the particle swarm. The simulation results show that the proposed algorithm can quickly find the optimal allocation scheme of tactical communication support tasks. © 2018, Editorial Office of Control and Decision. All right reserved.
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页码：1575 / 1583
页数：8
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  • [11] Luo L., Chen K., Du F.P., Surface fitting and position-pose measurements based on an improved SA-PSO algorithm, J of Tsinghua University:Science and Technology, 55, 10, pp. 1061-1066, (2015)
  • [12] Yan Z.P., Deng C., Zhao Y.F., Application of improved particle swarm algorithm to UUV 3-D path planning, J of Huazhong University of Science and Technology:Natural Science Edition, 41, 12, pp. 64-68, (2013)
  • [13] Xu S.H., Song M.L., Xu C., Training algorithm of process neural networks based on hybrid error gradient descent, J of Northeast Petroleum University, 38, 4, pp. 92-96, (2014)
  • [14] Tang K.Z., Li H.Y., Li J., Improved particle swarm optimization algorithm for solving complex optimization problems, J of Nanjing University of Science and Technology, 39, 4, pp. 386-391, (2015)
  • [15] Riget R., Vesterstrom J.S., A diversity-guided particle swarm optimizer-he ARPSO, (2002)
  • [16] Tang K.Z., Improvement and application of genetic algorithm and particle swarm algorithm research, (2012)
  • [17] Zhou L.J., Peng W., Zeng X.Q., Dynamic particle swarm optimization based on hybrid variable, Computer Science, 40, 11, pp. 143-146, (2013)
  • [18] Bai J.Q., Yin G.L., Sun Z.W., Random weighted hybrid particle swarm optimization algorithm based on second order oscillation and natural selection, Control and Decision, 27, 10, pp. 1459-1465, (2012)
  • [19] Wei J.X., Sun Y.H., Su X.N., A novel particle swarm optimization based on immune selection, J of Nanjing University:Natural Sciences, 46, 1, pp. 1-8, (2010)
  • [20] Zhan Z.H., Zhang J., Li Y., Adaptive particle swarm optimization, IEEE Trans on Systems, Man, and Cybernetics, Part B:Cybernetics, 39, 6, pp. 1362-1381, (2009)
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