An ensemble bat algorithm for large-scale optimization

被引：54

作者：

Cai, Xingjuan ^{[1
]}

Zhang, Jiangjiang ^{[1
]}

Liang, Hao ^{[1
]}

Wang, Lei ^{[2
]}

Wu, Qidi ^{[2
]}

机构：

[1] TaiYuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China

[2] Tongji Univ, Dept Control Sci & Engn, Shanghai 201804, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2019年 / 10卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Bat algorithm; Large-scale optimization; Ensemble strategy; Benchmark function; PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION ALGORITHM; CUCKOO SEARCH ALGORITHM; MUTATION; STRATEGY; SELECTION;

D O I：

10.1007/s13042-019-01002-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

It is difficult for the bat algorithm (BA) to retain good performance with increasing problem complexity and problem. In this paper, an ensemble BA is proposed to solve large-scale optimization problems (LSOPs) by introducing the integration ideas. The characteristics of six improved BA strategies are taken into account for the ensemble strategies. To fuse these strategies perfectly, the probability selection mechanisms, including the constant probability and dynamic probability, are designed by adjusting the odds of different strategies. To verify the performance of the algorithm in this paper, the proposed algorithm is applied to solve numerical optimization problems on benchmark functions with different dimensions. Then, the best ensemble BA is selected by comparing the constant probabilities and dynamic probabilities. The selected algorithm is compared with other excellent swarm intelligence optimization algorithms. Additionally, the superiority of the proposed algorithm is confirmed for solving LSOPs.

引用

页码：3099 / 3113

页数：15

共 55 条

[21] Evolutionary programming using a mixed mutation strategy [J].

Dong, Hongbin ;

He, Jun ;

Huang, Houkuan ;

Hou, Wei .

INFORMATION SCIENCES, 2007, 177 (01) :312-327

[22]

Gandomi A., 2013, ENG COMPUT-GERMANY, V29, P245, DOI [10.1007/s00366-012-0308-4, DOI 10.1007/S00366-012-0308-4]

[23] Sensitivity analysis in linear optimization: Invariant support set intervals [J].

Hadigheh, AG ;

Terlaky, T .

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 169 (03) :1158-1175

[24] Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm [J].

Jiang, M. ;

Luo, Y. P. ;

Yang, S. Y. .

INFORMATION PROCESSING LETTERS, 2007, 102 (01) :8-16

[25]

Khan K, 2011, ADV INTEL SOFT COMPU, V101, P59

[26] Artificial bee colony algorithm with gene recombination for numerical function optimization [J].

Li, Genghui ;

Cui, Laizhong ;

Fu, Xianghua ;

Wen, Zhenkun ;

Lu, Nan ;

Lu, Jian .

APPLIED SOFT COMPUTING, 2017, 52 :146-159

[27]

Liu Changping, 2013, CAAI Transactions on Intelligent Systems, V8, P240, DOI 10.3969/j.issn.1673-4785.201211047

[28] Object tracking with improved firefly algorithm [J].

Lv, Li ;

Fan, Tanghuai ;

Li, Qi ;

Sun, Zhen ;

Xu, Lizhong .

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2018, 9 (03) :219-231

[29] Differential evolution algorithm with ensemble of parameters and mutation strategies [J].

Mallipeddi, R. ;

Suganthan, P. N. ;

Pan, Q. K. ;

Tasgetiren, M. F. .

APPLIED SOFT COMPUTING, 2011, 11 (02) :1679-1696

[30] Ensemble strategies with adaptive evolutionary programming [J].

Mallipeddi, R. ;

Mallipeddi, S. ;

Suganthan, P. N. .

INFORMATION SCIENCES, 2010, 180 (09) :1571-1581

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