Global Chaotic Bat Optimization Algorithm

被引：4
|
作者：
Cui X.-T. [1 ,2 ]
Li Y. [1 ,2 ]
Fan J.-H. [1 ,2 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2020年 / 41卷 / 04期
关键词：
Bat algorithm; Chaotic mapping; Feature selection; Global optimum; Local optimum;
D O I：
10.12068/j.issn.1005-3026.2020.04.006
中图分类号：
学科分类号：
摘要：
In order to improve the accuracy of feature selection of bat algorithm, a global chaotic bat optimization algorithm(GCBA) was proposed. Firstly, GCBA adopts chaotic mapping method to enable the initialization of the population to traverse the entire solution space, obtain the optimal position of the bat, and make it more abundant. It solved the problem of initial population randomness. At the same time, GCBA introduces the optimal solution of the current particle and the optimal solution of the current population to jump out of local optimal solutions, which can effectively avoid the premature and improve the global search ability of the algorithm. The results of the bat algorithm(BA), particle swarm optimization(PSO) and genetic algorithm(GA) on 10 data sets showed that the proposed algorithm has higher classification accuracy and stronger ability to jump out of local optimum. © 2020, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：488 / 491and498
相关论文
共 11 条
  • [1] Yang X.S., A new metaheuristic bat-inspired algorithm, Computer Knowledge & Technology, 284, pp. 65-74, (2010)
  • [2] Tsai P.W., Pan J.S., Liao B.Y., Et al., Bat algorithm inspired algorithm for solving numerical optimization problems, Applied Mechanics and Materials, 148-149, pp. 134-137, (2012)
  • [3] Yang X.S., Bat algorithm for multi-objective optimization, International Journal of Bio-inspired Computation, 3, 5, pp. 267-274, (2012)
  • [4] Bora T.C., Coelho L.S., Lebensztajn L., Bat-inspired optimization approach for the brushless DC wheel motor problem, IEEE Transactions on Magnetics, 48, 2, pp. 947-950, (2012)
  • [5] Dash M., Liu H., Feature selection for classification, Intelligent Data Analysis, 1, 1-4, pp. 131-156, (1997)
  • [6] Yang J.-M., Ma M.-M., Che H.-J., Et al., Multi-objective adaptive chaotic particle swarm optimization algorithm, Control and Decision, 30, 12, pp. 2168-2174, (2015)
  • [7] Kazimipour B., Li X., Qin A.K., A review of population initialization techniques for evolutionary algorithms, Evolutionary Computation, pp. 2585-2592, (2014)
  • [8] Mirjalili S., Mirjalili S.M., Yang X.S., Binary bat algorithm, Neural Computing and Applications, 25, 3-4, pp. 663-681, (2014)
  • [9] Kennedy J., Eberhart R., Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, (2002)
  • [10] Kennedy J., Eberhart R.C., A discrete binary version of the particle swarm algorithm, IEEE International Conference on Systems, Man, and Cybernetics, pp. 4104-4108, (2002)
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