Skip to main content
SpringerLink
Log in

Global chaotic bat algorithm for feature selection

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The wrapper algorithm adopts the performance of the learning algorithm as the evaluation criteria to obtain excellent classification performance. However, the wrapper algorithm is prone to converge prematurely. A global chaotic bat algorithm (GCBA) is put up forward to improve this shortage. First, GCBA applies chaotic map to population initialization to cover the entire solution space. In addition, adaptive learning factors are presented to balance exploration and exploration. The learning factor of local optimal position gradually decreases in the early stage while the learning factor of global optimal position gradually increases in the later stage. Finally, to improve the exploitation, an improved transfer function is proposed, which transfers the continuous space to discrete binary space. GCBA is tested on 14 UCI data sets and 5 gene expression data sets compared with other 6 comparison algorithms. Compared with other algorithms, the results show that GCBA is able to achieve better classification performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article.

References

  1. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377

    Article  Google Scholar 

  2. Burke EK, Bykov Y (2016) The late acceptance hill-climbing heuristic. Eur J Oper Res 258(1):70–78

    Article  MathSciNet  MATH  Google Scholar 

  3. Dubois-Lacoste J, Lopez-Ibanez M, Stutzle T (2015) Anytime Pareto local search. Eur J Oper Res 243(2):369–385

    Article  MathSciNet  MATH  Google Scholar 

  4. Gerber M, Bornn L (2017) Improving simulated annealing through derandomization. J Global Optim 68(1):189–217

    Article  MathSciNet  MATH  Google Scholar 

  5. Agarwal D, Bharti PS (2021) Implementing modified swarm intelligence algorithm based on slime moulds for path planning and obstacle avoidance problem in mobile robots. Appl Soft Comput 4:107372

    Article  Google Scholar 

  6. Al-Roomi AR, El-Hawary ME (2016) Metropolis biogeography-based optimization. Inf Sci 360:73–95

    Article  Google Scholar 

  7. Wang YH, Liu XY, Xiang LS (2017) GA-based membrane evolutionary algorithm for ensemble clustering. Comput Intell Neurosci 2017:4367342

    Article  Google Scholar 

  8. Montiel O, Castillo O, Melin P et al (2007) Human evolutionary model: a new approach to optimization. Inf Sci 177(10):2075–2098

    Article  Google Scholar 

  9. Song XF, Zhang Y, Guo YN et al (2020) Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evol Comput 24(5):882–895

    Article  Google Scholar 

  10. Zhang Y, Gong DW, Gao XZ et al (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67–85

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhang Y, Cheng S, Shi YH et al (2019) Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Syst Appl 137:46–58

    Article  Google Scholar 

  12. Zhang Y, Song XF, Gong DW (2017) A return-cost-based binary firefly algorithm for feature selection. Inf Sci 418:561–574

    Article  Google Scholar 

  13. Hashim FA, Houssein EH, Mabrouk SM et al (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comp Syst 101:646–667

    Article  Google Scholar 

  14. Formato RA (2008) Central force optimization: a new nature Inspired computational framework for multidimensional search and optimization In: Nat Inspir Coop Strateg Optim (NICSO 2007)https://doi.org/10.1007/978-3-540-78987-1_21

  15. Abdechiri M, Meybodi MR, Bahrami H (2013) Gases brownian motion optimization: an algorithm for optimization (GBMO). Appl Soft Comput 13(5):2932–2946

    Article  Google Scholar 

  16. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  17. Alatas B (2012) A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Syst Appl 39(12):11080–11088

    Article  Google Scholar 

  18. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294

    Article  Google Scholar 

  19. Li X, Mao K, Lin F, Zhang X (2021) Particle swarm optimization with state-based adaptive velocity limit strategy. Neurocomputing 447:64–79

    Article  Google Scholar 

  20. Kashef S, Nezamabadi-Pour H (2015) An advanced ACO algorithm for feature subset selection. Neurocomputing 147:271–279

    Article  Google Scholar 

  21. Chen YP, Xu Q, Wang G, Zheng YF, Xu Q, Fan JH, Cui XT (2017) A novel bacterial foraging optimization algorithm for feature selection. Expert Syst Appl 83:1–17

    Article  Google Scholar 

  22. Mohapatra P, Chakravarty S, Dash PK (2015) An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol Comput 24:25–49

    Article  Google Scholar 

  23. Hussien AG, Houssein EH, Hassanien AE (2018) A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection In: Eighth International Conference On Intelligent Computing & Information Systems, pp 166–172

  24. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  25. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nat Inspir Coop Strat Optim 284:65–74

    MATH  Google Scholar 

  26. Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681

    Article  Google Scholar 

  27. Yilmaz S, Kucuksille EU (2015) A new modification approach on bat algorithm for solving optimization problems. Appl Soft Comput 28:259–275

    Article  Google Scholar 

  28. Gu Y, Budati C (2020) Energy-aware workflow scheduling and optimization in clouds using bat algorithm. Futur Gener Comp Syst 113(9):106–112

    Article  Google Scholar 

  29. Shareh MB, Bargh SH, Hosseinabadi A, Slowik A (2021) An improved bat optimization algorithm to solve the tasks scheduling problem in open shop. Neural Comput Appl 33(5):1559–1573

    Article  Google Scholar 

  30. Saad MS, Deri L, Shayfull Z, Nasir SM, Fathullah M Parameter estimation of damped compound pendulum using bat algorithm In: 2nd international conference on green design and manufacture, (2016) 78, pp 01118

  31. Mishra S, Shaw K, Mishra D (2012) A new meta-heuristic bat inspired classification approach for microarray data. In: 26th anniversary world congress on biosensors, 4(11):802–806

  32. Ahmad AO, Tajudin KA, Al-Betar B, Mohammad AL (2017) Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. Int J Data Min Bioinform 19(1):32–51

    Article  Google Scholar 

  33. Ashish T, Kapil S, Manju B (2018) Parallel bat algorithm-based clustering using mapreduce. In: Lecture notes on data engineering and communications technologies, pp 73–82

  34. Al-Betar MA, Awadallah MA, Faris H, Yang XS, Khader AT, Alomari OA (2018) Bat-inspired algorithms with natural selection mechanisms for global optimization. Neurocomputing 273:448–465

    Article  Google Scholar 

  35. Zhou L, Chen Y, Cao C, Chu Y, Lu H (2021) Macro-micro mutual learning inside compositional model for human pose estimation. Neurocomputing 449:176–188

    Article  Google Scholar 

  36. Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    Article  MathSciNet  Google Scholar 

  37. Luo QF, Zhou YQ, Xie J, Ma MZ, Li LL (2014) Discrete bat algorithm for optimal problem of permutation flow shop scheduling. Sci World J 2014:630280

    Article  Google Scholar 

  38. Afrabandpey H, Ghaffari M, Mirzaei A, Safayani M (2014) A novel Bat Algorithm based on chaos for optimization tasks. In: Iranian Conference on Intelligent Systems, pp 1–6

  39. Murugan R, Mohan MR, Rajan CCA, Sundari PD, Arunachalam S (2018) Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch. Appl Soft Comput 72:189–217

    Article  Google Scholar 

  40. Al-Betar MA, Awadallah MA (2018) Island bat algorithm for optimization. Expert Syst Appl 107:126–145

    Article  Google Scholar 

  41. Shehab M, Khader HT, Laouched M, Alomari OA (2019) Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. J Supercomput 75(5):2395–2422

    Article  Google Scholar 

  42. Alsalibi B, Abualigah L, Khader AT (2021) A novel bat algorithm with dynamic membrane structure for optimization problems. Appl Intell 51(4):1992–2007

    Article  Google Scholar 

  43. Cui XT, Li Y, Fan JH (2020) Global chaotic bat optimization algorithm. J Northeast Univ 41(4):488–491

    Google Scholar 

  44. Fan JH, Li Y, Wang T (2021) An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism. PLoS ONE 16(11):e0260725

    Article  Google Scholar 

  45. Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481

    Article  Google Scholar 

  46. Kennedy J, Eberhart R (2002) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp 1942–1948

  47. Alickovic E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and rotation forest. Neural Comput Appl 28(4):753–763

    Article  Google Scholar 

  48. Saleem N, Zafar K, Sabzwari AF (2019) Enhanced feature subset selection using niche based bat algorithm. Computation 7(3):49

    Article  Google Scholar 

  49. Balakrishnan K, Dhanalakshmi R, Khaire UM (2021) Improved salp swarm algorithm based on the levy flight for feature selection. J Supercomput. pp 1–21

  50. Kilic F, Kaya Y, Yildirim S (2021) A novel multi population based particle swarm optimization for feature selection. Knowl-Based Syst 219(4):106894

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, China

    Ying Li, Xueting Cui & Jiahao Fan

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, China

    Ying Li, Xueting Cui & Jiahao Fan

  3. Northeast Asian Research Center, Jilin University, Changchun, China

    Tan Wang

Authors
  1. Ying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xueting Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiahao Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiahao Fan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Cui, X., Fan, J. et al. Global chaotic bat algorithm for feature selection. J Supercomput 78, 18754–18776 (2022). https://doi.org/10.1007/s11227-022-04606-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04606-0

Keywords

Search

Navigation