Skip to main content
SpringerLink
Log in

An ensemble bat algorithm for large-scale optimization

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

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.

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

Similar content being viewed by others

References

  1. Yu WX, Wang J (2018) A new method to solve optimisation problems via fixed point of firefly algorithm. Int J Bio-Inspired Comput 11(4):249–256

    Article  Google Scholar 

  2. Wang G-G, Deb S, Coelho LDS (2018) Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio-Inspired Comput 12(1):1–22

    Article  Google Scholar 

  3. Jie W, Jiangjun Y (2018) A high-efficient multi-deme genetic algorithm with better load-balance. Int J Comput Sci Math 9(3):240–246

    Article  Google Scholar 

  4. Abdel-Baset M, Zhou Y, Ismail M (2018) An improved cuckoo search algorithm for integer programming problems. Int J Comput Sci Math 9(1):66–81

    Article  MathSciNet  Google Scholar 

  5. Arloff W, Schmitt RK, Venstrom LJ (2018) A parameter estimation method for stiff ordinary differential equations using particle swarm optimisation. Int J Comput Sci Math 9(5):419–432

    Article  MathSciNet  Google Scholar 

  6. Raj ED, Babu LDD (2018) A firefly inspired game dissemination and QoS-based priority pricing strategy for online social network games. Int J Bio-Inspired Comput 11(3):202–217

    Article  Google Scholar 

  7. Lv L, Fan T, Li Q, Sun Z, Lizhong X (2018) Object tracking with improved firefly algorithm. Int J Comput Sci Math 9(3):219–231

    Article  MathSciNet  Google Scholar 

  8. Yu G, Feng Y (2018) Improving firefly algorithm using hybrid strategies. Int J Comput Sci Math 9(2):163–170

    Article  MathSciNet  Google Scholar 

  9. Cui Z, Sun B, Xue Y, Wang G, Chen J (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distrib Comput 103:42–52

    Article  Google Scholar 

  10. Niu Y, Tian Z, Zhang M, Cai X, Li J (2018) Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction. Int J Comput Sci Math 9(6):547–554

    Article  MathSciNet  Google Scholar 

  11. Zhang M, Wang H, Cui Z, Chen J (2018) Hybrid multi-objective cuckoo search with dynamical local search. Memet Comput 10:199–208

    Article  Google Scholar 

  12. Cortés P, Muñuzuri J, Onieva L, Guadix J (2018) A discrete particle swarm optimisation algorithm to operate distributed energy generation networks efficiently. Int J Bio-Inspired Comput 12(4):226–235

    Article  Google Scholar 

  13. Bougherara M, Nedjah N, De LZ (2018) IP assignment for efficient NoC-based system design using multi-objective particle swarm optimisation. Int J Bio-Inspired Comput 12(4):203–213

    Article  Google Scholar 

  14. Cui Z, Zhang J, Wang Y, Cao Y, Cai X, Zhang W, Chen J (2019) A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci China Inf Sci 62(7):070212. https://doi.org/10.1007/s11432-018-9729-5

    Article  Google Scholar 

  15. Li G, Cui L, Fu X, Wen Z, Lu N, Lu J (2017) Artificial bee colony algorithm with gene recombination for numerical function optimization. Appl Soft Comput 52:146–159

    Article  Google Scholar 

  16. Cui Z, Li F, Zhang W (2019) Bat algorithm with principal component analysis. Int J Mach Learn Cybern 10(3):603–622

    Article  Google Scholar 

  17. Cai X, Gao X, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-inspired Comput 8(4):205–214

    Article  Google Scholar 

  18. Cui L, Li G, Zhu Z, Lin Q, Wong K-C, Chen J, Lu N, Lu J (2018) Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. Inf Sci 422:122–143

    Article  MathSciNet  Google Scholar 

  19. Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhou G, Zhao R, Zhou Y (2018) Solving large-scale 0-1 knapsack problem by the social-spider optimisation algorithm. Int J Comput Sci Math 9(5):433–441

    Article  MathSciNet  Google Scholar 

  21. Cui Z, Du L, Wang P, Cai X, Zhang W (2019) Malicious code detection based on CNNs and multi-objective algorithm. J Parallel Distrib Comput 129:50–58

    Article  Google Scholar 

  22. Wang P, Xue F, Li H, Cui Z, Xie L, Chen J (2019) A multi-objective DV-Hop localization algorithm based on NSGA-II in internet of things. Mathematics 7(2):184

    Article  Google Scholar 

  23. Cui Z, Chang Y, Zhang J, Cai X, Zhang W (2019) Improved NSGA-III with selection-and-elimination operator. Swarm Evol Comput 49:23–33

    Article  Google Scholar 

  24. Cai X, Wang P, Lei D, Cui Z, Zhang W, Chen J (2019) Multi-objective 3-dimensional DV-Hop localization algorithm with NSGA-II. IEEE Sens J. https://doi.org/10.1109/JSEN.2019.2927733

    Article  Google Scholar 

  25. Wang P, Huang J, Cui Z, Xie L, Chen J (2019) A Gaussian error correction multi-objective positioning model with NSGA-II. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.5464

    Article  Google Scholar 

  26. Wang R, Zhang F, Zhang T, Fleming PJ (2018) Cooperative co-evolution with improved differential grouping method for large-scale global optimisation. Int J Bio-Inspired Comput 12(4):214–225

    Article  Google Scholar 

  27. Amiri E, Dehkordi MN (2018) Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic. Int J Bio-Inspired Comput 12(3):164–172

    Article  Google Scholar 

  28. Zhang J, Xue F, Cai X, Cui Z, Chang Y, Zhang W (2019) W Li Privacy protection based on many-objective optimization algorithm. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.5342

    Article  Google Scholar 

  29. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  30. Hongbin D, Jun H, Houkuan H, Wei H (2007) Evolutionary programming using a mixed mutation strategy. Inf Sci 177:312–327

    Article  MathSciNet  MATH  Google Scholar 

  31. Mallipeddi RS, Ponnuthurai N (2010) Ensemble of Constraint Handling Techniques. IEEE Trans Evol Comput 14:561–579

    Article  Google Scholar 

  32. Saranya G, Nehemiah HK, Kannan A (2018) Hybrid particle swarm optimisation with mutation for code smell detection. Int J Bio-Inspired Comput 12(3):186–195

    Article  Google Scholar 

  33. Yu EL, Suganthan PNJIS (2010) Ensemble of niching algorithms. Inf Sci 180:2815–2833

    Article  MathSciNet  Google Scholar 

  34. Tasgetiren MF, Suganthan PN, Pan QK (2010) An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem. Appl Math Comput 215:3356–3368

    MathSciNet  MATH  Google Scholar 

  35. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11:1679–1696

    Article  Google Scholar 

  36. Zhao SZ, Suganthan PN, Zhang Q (2012) Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans Evol Comput 16:442–446

    Article  Google Scholar 

  37. Zhao B, Xue Y, Xu B, Ma T, Liu J (2018) Multi-objective classification based on NSGA-II. Int J Comput Sci Math 9(6):539–546

    Article  MathSciNet  Google Scholar 

  38. Mallipeddi R, Mallipeddi S, Suganthan PN (2010) Ensemble strategies with adaptive evolutionary programming. Inf Sci 180:1571–1581

    Article  MATH  Google Scholar 

  39. Wang H, Zhijian W, Rahnamayan S, Sun H, Liu Y, Pan J (2014) Multi-strategy ensemble artificial bee colony algorithm. Inf Sci 279:587–603

    Article  MathSciNet  MATH  Google Scholar 

  40. Tasgetiren MF, Suganthan PN, Pan QK (2010) An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem. Appl Math Comput 215:3356–3368

    MathSciNet  MATH  Google Scholar 

  41. Cui Z, Cao Y, Cai X, Cai J, Chen J (2019) Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of things. J Parallel Distrib Comput 132:217–229

    Article  Google Scholar 

  42. Wang G, Cai X, Cui Z, Min G, Chen J (2017) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Topics Comput. https://doi.org/10.1109/TETC.2017.2703784

    Article  Google Scholar 

  43. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Comput Knowl Technol 284:65–74

    MATH  Google Scholar 

  44. Cui Z, Xue F, Cai X, Cao Y, Wang G, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Ind Inf 14(7):3178–3196

    Article  Google Scholar 

  45. Khan K, Nikov A, Sahai A (2011) A fuzzy bat clustering method for ergonomic screening of office workplaces. In: Third international conference on software, services and semantic technologies S3T 2011. Bourgas, Bulgaria, pp 59–66

    Google Scholar 

  46. Bahmani-Firouzi B, Azizipanah-Abarghooee R (2014) Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int J Electr Power Energy Syst 56:42–54

    Article  Google Scholar 

  47. Cai X, Wang H, Cui Z, Cai J, Xue Y, Wang L (2018) Bat algorithm with triangle-flipping strategy for numerical optimization. Int J Mach Learn Cybern 9(2):199–215

    Article  Google Scholar 

  48. Zhang M, Cui Z, Chang Y, Ren Y, Cai X, Wang H (2018) Bat algorithm with individual local search. In: International conference on intelligence science, pp 442–451

    Chapter  Google Scholar 

  49. Cai X, Wang L, Kang Q, Qidi W (2014) Bat algorithm with Gaussian walk. Int J Bio-Inspired Comput 6(3):166–174

    Article  Google Scholar 

  50. Hadigheh AG, Terlaky T (2006) Sensitivity analysis in linear optimization: invariant support set intervals. Eur J Oper Res 169(3):1158–1175

    Article  MathSciNet  MATH  Google Scholar 

  51. Wimalajeewa T, Varshney PK (2017) Sparse signal detection with compressive measurements via partial support set estimation. IEEE Trans Signal Inf Process Over Netw 3(1):46–60

    Article  MathSciNet  Google Scholar 

  52. Jiang M, Luo YP, Yang SY (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf Process Lett 102(1):8–16

    Article  MathSciNet  MATH  Google Scholar 

  53. Chih M, Lin C-J, Chern M-S, Ou T-Y (2014) Particle swarm optimization with time-varying acceleration coefficients for the multidimensional knapsack problem. Appl Math Model 38(4):1338–1350

    Article  MathSciNet  MATH  Google Scholar 

  54. Amir GH, Xin-She Y, Amir AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(2):245

    Article  Google Scholar 

  55. Changping L, Chunming Y (2013) Bat algorithm with the characteristics of Lévy flights. CAAI Trans Intell Syst 8(3):240–246

    Google Scholar 

  56. Jian X, Yong-Quan Z, Huan C (2013) A bat algorithm based on Lévy flights trajectory. Pattern Recognit Artif Intell 26(9):829–837

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61806138, Natural Science Foundation of Shanxi Province under Grant No. 201801D121127, Taiyuan University of Science and Technology Scientific Research Initial Funding under Grant No. 20182002.

Author information

Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, TaiYuan University of Science and Technology, Taiyuan, 030024, China

    Xingjuan Cai, Jiangjiang Zhang & Hao Liang

  2. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Lei Wang & Qidi Wu

Authors
  1. Xingjuan Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiangjiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qidi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingjuan Cai.

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

Cai, X., Zhang, J., Liang, H. et al. An ensemble bat algorithm for large-scale optimization. Int. J. Mach. Learn. & Cyber. 10, 3099–3113 (2019). https://doi.org/10.1007/s13042-019-01002-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-019-01002-8

Keywords

Search

Navigation