A solution to resource allocation problem based on discrete grey wolf optimizer

被引：0
|
作者：
Xiang Z. [1 ]
Yang J. [1 ]
Li H. [1 ]
Liang X. [1 ]
机构：
[1] School of Transportation, Wuhan University of Technology, Wuhan
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2021年 / 49卷 / 08期
关键词：
Discrete grey wolf optimizer; Genetic algorithm; Matthew effect; Repair and optimization method; Resource allocation problem;
D O I：
10.13245/j.hust.210815
中图分类号：
学科分类号：
摘要：
As the original grey wolf optimizer (GWO) can only solve continuous optimization problems, but cannot directly solve the resource allocation problem in the discrete domain, a discrete grey wolf optimizer (DGWO) based on the Matthew effect was proposed to solve the resource allocation problem. Firstly, according to the mathematical mapping idea, a coding conversion method was given, which converted continuous search space into discrete search space and real numbers into integers. Then, the infeasible solution was treated by repairing and optimizing method based on Matthew effect. Finally, the results of DGWO were compared with those of genetic algorithm, and it can be seen that no matter the convergence speed or the solution quality, DGWO is superior to the genetic algorithm. The experimental results show the feasibility, correctness and superiority of DGWO for the resource allocation problem solving. © 2021, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：81 / 85
页数：4
相关论文
共 16 条
  • [1] (2016)
  • [2] 4
  • [3] 2
  • [4] DANG Y J, LI Y Q, QIN Y H, Et al., Optimal defense resource allocation for attacks in wireless sensor networks based on risk assessment model[J], Chaos Solitons & Fractals, 137, 5, pp. 1-8, (2020)
  • [5] POPP R L, PATTIPATI K R, BARS Y., M-best S-D assignment algorithm with application to multitarget tracking, IEEE Transactions on Aerospace and Electronic Systems, 37, 1, pp. 22-39, (2001)
  • [6] STORMSPP A, SPIEKSMA F C R., An LP-based algorithm for the data association problem in multitarget tracking[J], Computers & Operations Research, 30, 7, pp. 1067-1085, (2003)
  • [7] BELIAVSKY G I, DANILOVA N V, OUGOLNITSKY G A., Evolutionary methods for solving dynamic resource allocation problems[J], Automation and Remote Control, 80, 7, pp. 1335-1346, (2019)
  • [8] MIRJALILI S, MIRJALIIIS M, LEWIS A., Grey wolf optimization, Advances in Engineering Software, 69, 7, pp. 46-61, (2014)
  • [9] FARIS H, ALJARAH I, Al-BETAR M A, Et al., Grey wolf optimizer:a review of recent variants andapplic ations, Neural Computing and Applications, 30, 2, pp. 413-435, (2017)
  • [10] NASRABADI M S, SHARAFI Y, TAYARI M., A parallel grey wolf optimizer combined with opposition based learning, Proc of 1st Conference on Swarm Intelligence and Evolutionary Computation, pp. 18-23, (2016)
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