Skip to main content
SpringerLink
Log in

Innovative hybrid metaheuristic algorithms: exponential mutation and dual-swarm strategy for hybrid feature selection problem

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Feature selection is an important pre-processing step aiming to reduce the number of features and increase feature space quality. This step helps increase the classification performance, which plays a critical role in machine learning and data mining. Metaheuristic algorithms are increasingly used for feature selection problems due to their robustness and searchability. Here, two nature-inspired Hybrid optimization algorithms, PSOHHO and its variant PSOHHO-V, are proposed. Here, the concepts of dual-swarm strategy and exponential mutation operator are introduced to enhance the exploration power of the proposed algorithms. The exponential mutation operator, which calculates the likelihood of mutation per particle depending on the current iteration and its history, is the improvement. Theoretical analysis of the proposed algorithm is carried out by introducing the Signature of the proposed algorithm PSOHHO-V. On Benchmark Functions, the effectiveness of the suggested strategies is evaluated and compared with other metaheuristic algorithms. Then, the proposed algorithms are applied to a Hybrid filter-wrapper Feature selection problem. Then, they are compared with other traditional and recent metaheuristic algorithms on seven UCI machine learning repository datasets. Here, we observe from the statistical results and the convergence curves that the proposed algorithms give better results than the traditional and recent metaheuristic algorithms.

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
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

Data is open access and available in the reference no. 29.

References

  1. Farshi TR, Orujpour M (2019) Multi-level image thresholding based on social spider algorithm for global optimization. Int J Inf Technol 11(4):713–718. https://doi.org/10.1007/s41870-019-00328-4

    Article  Google Scholar 

  2. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483. https://doi.org/10.1108/02644401211235834

    Article  Google Scholar 

  3. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  4. Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  5. Aggarwal D, Kumar V (2021) Performance evaluation of distance metrics on Firefly Algorithm for VRP with time windows. Int J Inf Technol 13(6):2355–2362. https://doi.org/10.1007/s41870-019-00387-7

    Article  Google Scholar 

  6. Mirjalili S, Lewis A (2016) Grey Wolf Optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  7. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  8. Mangla C, Ahmad M, Uddin M (2021) Optimization of complex nonlinear systems using genetic algorithm. Int J Inf Technol 13(5):1913–1925. https://doi.org/10.1007/s41870-020-00421-z

    Article  Google Scholar 

  9. Mahapatra AK, Panda N, Pattanayak BK (2023) Quantized Salp Swarm Algorithm (QSSA) for optimal feature selection. Int J Inf Technol 15(2):725–734. https://doi.org/10.1007/s41870-023-01161-6

    Article  Google Scholar 

  10. Sadeghi H, Ajoudanian S (2022) Optimized feature selection in software product lines using Discrete Bat Algorithm. Int J Comput Intell Appl. https://doi.org/10.1142/S1469026822500031

    Article  Google Scholar 

  11. Dutta D, Rath S (2022) Job scheduling on computational grids using multi-objective fuzzy particle swarm optimization. In: Soft computing: theories and applications: proceedings of SoCTA, vol. 1, Springer, pp 333–347. https://doi.org/10.1007/978-981-16-1740-9_28

  12. Zhang Q, Chen H, Luo J, Xu Y, Wu C, Li C (2018) Chaos enhanced bacterial foraging optimization for global optimization. IEEE Access 6:64905–64919. https://doi.org/10.1109/ACCESS.2018.2876996

    Article  Google Scholar 

  13. Luo J, Chen H, Xu Y, Huang H, Zhao X (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668. https://doi.org/10.1016/j.apm.2018.07.044

    Article  MathSciNet  Google Scholar 

  14. Opara KR, Arabas J (2019) Differential evolution: a survey of theoretical analyses. Swarm Evol Comput 44:546–558. https://doi.org/10.1016/j.swevo.2018.06.010

    Article  Google Scholar 

  15. Heidari AA, Faris H, Aljarah I, Mirjalili S (2019) An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23(17):7941–7958

    Article  Google Scholar 

  16. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp 1942–1948

  17. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66. https://doi.org/10.1109/4235.585892

    Article  Google Scholar 

  18. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  Google Scholar 

  19. Salcedo-Sanz S (2016) Modern meta-heuristics based on nonlinear physics processes: a review of models and design procedures. Phys Rep 655:1–70. https://doi.org/10.1016/j.physrep.2016.08.001

    Article  MathSciNet  Google Scholar 

  20. Ranjan R, Chhabra JK (2023) Automatic feature selection using enhanced dynamic Crow Search Algorithm. Int J Inf Technol 15(5):2777–2782. https://doi.org/10.1007/s41870-023-01319-2

    Article  Google Scholar 

  21. Jovic A, Brkic K, Bogunovic N (2015) A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 1200–1205. https://doi.org/10.1109/MIPRO.2015.7160458

  22. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  23. Tran B, Xue B, Zhang M (2014) Overview of particle swarm optimization for feature selection in classification. In: Dick G, Browne WN, Whigham P, Zhang M, Bui LT, Ishibuchi H, Jin Y, Li X, Shi Y, Singh P, Tan KC, Tang K (eds) Asia-Pacific conference on simulated evolution and learning. Springer, Cham, pp 605–617. https://doi.org/10.1007/978-3-319-13563-2_51

    Chapter  Google Scholar 

  24. Dutta D, Rath S (2023) A hybrid swarm optimization with trapezoidal and pentagonal fuzzy numbers using benchmark functions. Int J Inf Technol 15(5):2747–2758. https://doi.org/10.1007/s41870-023-01301-y

    Article  Google Scholar 

  25. Molaei S, Moazen H, Najjar-Ghabel S, Farzinvash L (2021) Particle swarm optimization with an enhanced learning strategy and crossover operator. Knowl-Based Syst 215:106768. https://doi.org/10.1016/j.knosys.2021.106768

    Article  Google Scholar 

  26. Clerc M (2015) Guided randomness in optimization. Wiley, Hoboken, NJ, USA. https://doi.org/10.1002/9781119136439

    Book  Google Scholar 

  27. Chakraborty (2002) Genetic algorithm with fuzzy fitness function for feature selection. In: Proceedings of the IEEE International Symposium on Industrial Electronics ISIE-02, vol.1, pp 315–319. https://doi.org/10.1109/ISIE.2002.1026085

  28. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  29. Dua D, Casey G (2017) UCI machine learning repository, URL https://archive.ics.uci.edu/ml, vol. 7, no.1

Download references

Acknowledgements

This research work did not receive funding from any agency or organization.

Author information

Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology- Warangal, Hanumakonda, Telangana, 506004, India

    Debashis Dutta & Subhabrata Rath

Authors
  1. Debashis Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subhabrata Rath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subhabrata Rath.

Ethics declarations

Conflict of interest

The authors declared no conflicting interest regarding this paper’s authorship arrangement and publication.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, D., Rath, S. Innovative hybrid metaheuristic algorithms: exponential mutation and dual-swarm strategy for hybrid feature selection problem. Int. j. inf. tecnol. 16, 77–89 (2024). https://doi.org/10.1007/s41870-023-01649-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01649-1

Keywords

Search

Navigation