Skip to main content
SpringerLink
Log in

An Analysis on Hybrid Brain Storm Optimisation Algorithms

  • Conference paper
  • First Online:
Book cover Hybrid Artificial Intelligent Systems (HAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13469))

Included in the following conference series:

Abstract

Optimisation can be described as the process of finding optimal values for the variables of a given problem in order to minimise or maximise one or more objective function(s). Brain storm optimisation (BSO) algorithm is relatively new swarm intelligence algorithm that mimics the brainstorming process in which a group of people solves a problem together. The aim of this paper is to present hybrid BSO algorithm solutions in general, and particularly: (i) a hybrid BSO for improving the performances of the original BSO algorithm; (ii) a hybrid BSO for the flexible job-shop scheduling problem; and (iii) a feature selection by a hybrid BSO algorithm for the COVID-19 classification. The hybrid BSO algorithm overcomes the lack of exploitation in the original BSO algorithm, and simultaneously, the obtained better results prove their efficiency and robustness.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shi, Y.: An optimization algorithm based on brainstorming process. Int. J. Swarm Intell. Res. 2(4), 35–62 (2011). https://doi.org/10.4018/ijsir.2011100103

    Article  Google Scholar 

  2. Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. 46(4), 445–458 (2016). https://doi.org/10.1007/s10462-016-9471-0

    Article  Google Scholar 

  3. Guo, X., Wu, Y., Xie, L., Cheng, S., Xin, J.: An adaptive brain storm optimization algorithm for multiobjective optimization problems. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 365–372. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-94120-2_41

  4. Shi, Y., Xue, J., Wu, Y.: Multi-objective optimization based on brain storm optimization algorithm. Int. Swarm Intell. Res. 4(3), 1–21 (2013). https://doi.org/10.4018/ijsir.2013070101

    Article  Google Scholar 

  5. Simić, D., Ilin, V., Simić, S.D., Simić, S.: Swarm intelligence methods on inventory management. In: Graña, M., et al. (eds.) SOCO’18-CISIS’18-ICEUTE’18 2018. AISC, vol. 771, pp. 426–435. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94120-2_41

  6. Simić, D., Ilin, V., Svirčević, V., Simić, S.: A hybrid clustering and ranking method for best positioned logistics distribution centre in Balkan Peninsula. Logic J. IGPL 25(6), 991–1005 (2017). https://doi.org/10.1093/jigpal/jzx047

    Article  MathSciNet  Google Scholar 

  7. Simić, D., Svirčević, V., Ilin, V., Simić, S.D., Simić, S.: Particle swarm optimization and pure adaptive search in finish goods’ inventory management. Cybern. Syst. 50(1), 58–77 (2019). https://doi.org/10.1080/01969722.2018.1558014

    Article  Google Scholar 

  8. Simić, D., Svirčević, V., Corchado, E., Calvo-Rolle, J.L., Simić, S.D., Simić, S.: Modelling material flow using the Milk run and Kanban systems in the automotive industry. Expert. Syst. 38(1), e12546 (2021). https://doi.org/10.1111/exsy.12546

    Article  Google Scholar 

  9. Zayas-Gato, F., et al.: A hybrid one - class approach for detecting anomalies in industrial systems. Expert Syst. e12990 (2022). https://doi.org/10.1111/exsy.12990

  10. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991). https://doi.org/10.1016/B978-0-08-050684-5.50008-2

    Article  MathSciNet  Google Scholar 

  11. Cheng, S., Shi, Y., Qin, Q., Ting, T.O., Bai, R.: Maintaining population diversity in brain storm optimization algorithm. In: Proceedings of 2014 IEEE Congress on Evolutionary Computation (CEC 2014), pp. 3230–3237. IEEE, Beijing (2014)

    Google Scholar 

  12. Cao, Z., Rong, X., Du, Z.: An improved brain storm optimization with dynamic clustering strategy. MATEC Web Conf. 95, 19002 (2017). https://doi.org/10.1051/matecconf/20179519002

    Article  Google Scholar 

  13. Liu, J., Peng, H., Wu, Z., Chen, J., Deng, C.: Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment. Appl. Intell. 50(4), 1289–1315 (2020). https://doi.org/10.1007/s10489-019-01600-7

    Article  Google Scholar 

  14. Alzaqebah, M., Jawarneh, S., Alwohaibi, M., Alsmadi, M.K., Almarashdeh, I., Mohammad, R.M.A.: Hybrid brain storm optimization algorithm and late acceptance hill climbing to solve the flexible job-shop scheduling problem. J. King Saud Univ. – Comput. Inf. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.09.004

  15. Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1(2), 117–129 (1976)

    Article  MathSciNet  Google Scholar 

  16. Genova, K., Kirilov, L., Guliashki, V.: A survey of solving approaches for multiple objective flexible job shop scheduling problems. Cybern. Inf. Technol. 15(2), 3–22 (2015). https://doi.org/10.1515/cait-2015-0025

    Article  MathSciNet  Google Scholar 

  17. Bholowalia, P., Kumar, A.: EBK-means: a clustering technique based on elbow method and k-means in WSN. Int. J. Comput. Appl. 105(9) (2014). https://doi.org/10.5120/18405-9674

  18. Bezdan, T., Živković, M., Bacanin, N., Chhabra, A., Suresh, M.: Feature selection by hybrid brain storm optimization algorithm for COVID-19 classification. J. Comput. Biol. 29(6), 1–15 (2022). https://doi.org/10.1089/cmb.2021.0256

    Article  MathSciNet  Google Scholar 

  19. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14

  20. Dua, D., Graff, C: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Accessed 28 Sept 2020

  21. https://github.com/Atharva-Peshkar/Covid-19-Patient-Health-Analytics. Accessed 25 Sept 2020

  22. Iwendi, C., et al.: COVID-19 patient health prediction using boosted random forest algorithm. Front. Public Health 8, 357 (2020). https://doi.org/10.3389/fpubh.2020.00357

  23. Mohamed, A.W., Hadi, A.A., Mohamed, A.K.: Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int. J. Mach. Learn. Cybern. 11(5), 1501–1529 (2020). https://doi.org/10.1007/s13042-019-01053-x

    Article  Google Scholar 

  24. Agrawal, P., Ganesh, T., Mohamed, A.W.: Chaotic gaining sharing knowledge-based optimization algorithm: an improved metaheuristic algorithm for feature selection. Soft. Comput. 25(14), 9505–9528 (2021). https://doi.org/10.1007/s00500-021-05874-3

    Article  Google Scholar 

  25. Agrawal, P., Ganesh, T., Mohamed, A.W.: A novel binary gaining-sharing knowledge-based optimization algorithm for feature selection. Neural Comput. Appl. 33(11), 5989–6008 (2021). https://doi.org/10.1007/s00521-020-05375-8

    Article  Google Scholar 

  26. Agrawal, P., Ganesh, T., Oliva, D., Mohamed, A.W.: S-shaped and v-shaped gaining-sharing knowledge-based algorithm for feature selection. Appl. Intell. 52(1), 81–112 (2022). https://doi.org/10.1007/s10489-021-02233-5

    Article  Google Scholar 

  27. Too, J., Mirjalili, S.: A hyper learning binary dragonfly algorithm for feature selection: a COVID-19 case study. Knowl. Based Syst. 212, 106553 (2021). https://doi.org/10.1016/j.knosys.2020.106553

    Article  Google Scholar 

  28. Khalilpourazari, S., Doulabi, H.H., Çiftçioğluc, A.O., Weber, G.-W.: Gradient-based grey wolf optimizer with Gaussian walk: application in modelling and prediction of the COVID-19 pandemic. Expert Syst. Appl. 177, 114920 (2021)

    Article  Google Scholar 

  29. Canayaz, M., Şehribanoğlu, S., Özdağ, R., Demir, M.: COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms. Neural Comput. Appl. 34(7) (2022). https://doi.org/10.1007/s00521-022-07052-4

  30. Alali, Y., Harrou, F., Sun, Y.: A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci. Rep. 12(1), 2467 (2022). https://doi.org/10.1038/s41598-022-06218-3

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Dragan Simić & Svetislav D. Simić

  2. Frontiers Media SA, Paseo de Castellana 77, Madrid, Spain

    Zorana Banković

  3. University of Oviedo, Campus de Llamaquique, 33005, Oviedo, Spain

    José R. Villar

  4. Department of Industrial Engineering, University of A Coruña, 15405, Ferrol-A Coruña, Spain

    José Luis Calvo-Rolle

  5. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 1–9, 21000, Novi Sad, Serbia

    Svetlana Simić

Authors
  1. Dragan Simić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zorana Banković
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José R. Villar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Luis Calvo-Rolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Svetislav D. Simić
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Svetlana Simić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dragan Simić .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Analytics Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  8. Universidad Pablo de Olavide, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Simić, D., Banković, Z., Villar, J.R., Calvo-Rolle, J.L., Simić, S.D., Simić, S. (2022). An Analysis on Hybrid Brain Storm Optimisation Algorithms. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15471-3_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15470-6

  • Online ISBN: 978-3-031-15471-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation