Skip to main content
SpringerLink
Log in

Applications and Advancements of Firefly Algorithm in Classification: An Analytical Perspective

  • Conference paper
  • First Online:
Computational Intelligence in Pattern Recognition

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 999))

Abstract

Nature-inspired algorithms, particularly those located on swarm intelligence and population-based, have dragged much attention in the last few years. Firefly algorithm is one of the leading swarm-based algorithms, which came into view about 10 years ago. It has become progressively an essential appliance of swarm intelligence that has been enforced in many vicinities of optimization along with the engineering practice. Abundant difficulties from distinctive regions have been solved effectively by using firefly algorithm and its development. In this paper, we conducted a brief study on the applications and advancements of firefly algorithm in the area of data classification. Various classification areas, such as image classification, text classification, neural network-based classification and some other classifications, are taken into consideration for this study. Special attention has been paid toward the usage level and implementation issues of firefly algorithm in different classification domains. The main aim of this survey is to inspire researchers toward further research of firefly algorithm in several other application areas other than the intended area.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Similar content being viewed by others

References

  1. Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Handbook of Metaheuristics, pp. 250–285. Springer, Boston (2003)

    MATH  Google Scholar 

  2. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston, MA (2011)

    Google Scholar 

  3. Yang, X.-S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)

    Article  Google Scholar 

  4. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)

    Chapter  Google Scholar 

  5. Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)

    Article  Google Scholar 

  6. Macioek, P., Dobrowolski, G.: Using shallow semantic analysis and graph modeling for document classification. Int. J. Data Min. Model. Manag. 5(2), 123–137 (2013)

    Google Scholar 

  7. Yin, P., Wang, H., Zheng, L.: Sentiment classification of Chinese online reviews: analyzing and improving supervised machine learning. Int. J. Web Eng. Technol. 7(4), 381–398 (2012)

    Article  Google Scholar 

  8. Kanungo, D.P., Naik, B., Nayak, J., Baboo, S., Behera, H.S.: An improved PSO based back propagation learning-MLP (IPSO-BP-MLP) for classification. In: Computational Intelligence in Data Mining, vol. 1, pp. 333–344. Springer, India (2015)

    Google Scholar 

  9. Uysal, A.K., Gunal, S.: Text classification using genetic algorithm oriented latent semantic features. Exp. Syst. Appl. 41(13), 5938–5947 (2014)

    Article  Google Scholar 

  10. Sriramkumar, D., Malmathanraj, R., Mohan, R., Umamaheswari, S.: Mammogram tumour classification using modified segmentation techniques. Int. J. Biomed. Eng. Technol. 13(3), 218–239 (2013)

    Article  Google Scholar 

  11. Kianmehr, K., Alshalalfa, M., Alhajj, R.: Fuzzy clustering-based discretization for gene expression classification. Knowl. Inf. Syst. 24(3), 441–465 (2010)

    Article  Google Scholar 

  12. Sarkar, B.K., Sana, S.S., Chaudhuri, K.: Accuracy-based learning classification system. Int. J. Inf. Decis. Sci. 2(1), 68–86 (2010)

    Google Scholar 

  13. Valavanis, I.K., Spyrouand, G.M., Nikita, K.S.: A comparative study of multi classification methods for protein fold recognition. Int. J. Comput. Intell. Bioinf. Syst. Biol. 1(3), 332–346 (2010)

    Google Scholar 

  14. Nayak, J., Naik, B., Behera, H.S.: A novel nature inspired firefly algorithm with higher order neural network: performance analysis. Eng. Sci. Technol. Int. J. 19(1), 197–211 (2016)

    Article  Google Scholar 

  15. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  16. Hassanzadeh, T., Faez, K., Seyfi, G.: A speech recognition system based on structure equivalent fuzzy neural network trained by firefly algorithm. In: 2012 International Conference on Biomedical Engineering (ICoBE), pp. 63–67. IEEE (2012)

    Google Scholar 

  17. Le Cun, Y.: A theoretical framework for back propagation. In: Touretzky, D., Hinton, G., Sejnowski, T., (eds.) Proceedings of the 1988 Connectionist Models Summer School. June 17–26, pp. 21–28. Morgan Kaufmann, San Mateo, CA (1988)

    Google Scholar 

  18. Hassim, Y.M.M., Ghazali, R.: Mammographic mass classification using functional link neural network with modified bee firefly algorithm. In: International Conference in Swarm Intelligence. Springer, Cham (2016)

    Google Scholar 

  19. Behera, N.K.S., Behera, H.S.: Firefly based ridge polynomial neural network for classification. In: 2014 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). IEEE (2014)

    Google Scholar 

  20. Alweshah, M., Abdullah, S.: Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems. Appl. Soft Comput. 35, 513–524 (2015)

    Article  Google Scholar 

  21. Behera, S., Sahu, B.: Non linear dynamic system identification using Legendre neural network and firefly algorithm. In: 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE (2016)

    Google Scholar 

  22. Jinthanasatian, P., Auephanwiriyakul, S., Theera-Umpon, N.: Microarray data classification using neuro-fuzzy classifier with firefly algorithm. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (2017)

    Google Scholar 

  23. Zhang, L., et al.: Classifier ensemble reduction using a modified firefly algorithm: an empirical evaluation. Expert. Syst. Appl. 93, 395–422 (2018)

    Article  Google Scholar 

  24. Adewumi, O.A., Akinyelu, A.A.: A hybrid firefly and support vector machine classifier for phishing email detection. Kybernetes 45(6), 977–994 (2016)

    Article  MathSciNet  Google Scholar 

  25. Xu, H., et al.: An improved firefly algorithm for feature selection in classification. Wirel. Pers. Commun. 1–12

    Google Scholar 

  26. Hashem, M., Hassanein, A.S.: Jaw fracture classification using meta heuristic firefly algorithm with multi-layered associative neural networks. Clust. Comput. 1–8 (2018)

    Google Scholar 

  27. Sahmadi, B., et al. A modified firefly algorithm with support vector machine for medical data classification. In: Computational Intelligence and Its Applications: 6th IFIP TC 5 International Conference, CIIA 2018, Oran, Algeria, May 8–10, 2018, Proceedings 6. Springer International Publishing (2018)

    Google Scholar 

  28. Kumar, A., Khorwal, R.: Firefly algorithm for feature selection in sentiment analysis. In: Computational Intelligence in Data Mining, pp. 693–703. Springer, Singapore (2017)

    Google Scholar 

  29. Kalyani, G., Chandra Sekhara Rao, M.V.P., Janakiramaiah, B.: Privacy-preserving classification rule mining for balancing data utility and knowledge privacy using adapted binary firefly algorithm. Arab. J. Sci. Eng. 1–23 (2017)

    Google Scholar 

  30. Hassim, Y.M.M., Ghazali, R., Wahid, N.: Improved functional link neural network learning using modified bee-firefly algorithm for classification task. In: International Conference on Soft Computing and Data Mining. Springer, Cham (2016)

    Google Scholar 

  31. Lahiri, R., Rakshit, P., Konar, A.: Evolutionary perspective for optimal selection of EEG electrodes and features. Biomed. Signal Process. Control 36, 113–137 (2017)

    Article  Google Scholar 

  32. Anbu, M., Anandha Mala, G.S.: Feature selection using firefly algorithm in software defect prediction. Clust. Comput. 1–10 (2017)

    Google Scholar 

  33. Yamazaki, K., Matsushita, H., Jinno, M.: Virtualized-elastic-regenerator placement by firefly algorithm for translucent elastic optical networks. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE (2016)

    Google Scholar 

  34. Zhang, L., Shan, L., Wang, J.: Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion. Neural Comput. Appl. 28(9), 2795–2808 (2017)

    Article  Google Scholar 

  35. Behnam, M., Pourghassem, H.: Power complexity feature-based seizure prediction using DNN and firefly-BPNN optimization algorithm. In: 2015 22nd Iranian Conference on Biomedical Engineering (ICBME). IEEE (2015)

    Google Scholar 

  36. Rajakumar, B.R., George, A.: On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE (2013)

    Google Scholar 

  37. Darwish, S.M.: Combining firefly algorithm and Bayesian classifier: new direction for automatic multilabel image annotation. IET Image Process. 10(10), 763–772 (2016)

    Article  Google Scholar 

  38. Jothi, G.: Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification. Appl. Soft Comput. 46, 639–651 (2016)

    Article  Google Scholar 

  39. Keerthana, K., Veerasenthilkumar, G., Vasuki, S.: Firefly based band selection for hyperspectral image classification

    Google Scholar 

  40. Krawczyk, B., Filipczuk, P.: Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition. Eng. Appl. Artif. Intell. 31, 126–135 (2014)

    Article  Google Scholar 

  41. Su, H., Cai, Y.: Firefly algorithm optimized extreme learning machine for hyperspectral image classification. In: 2015 23rd International Conference on Geoinformatics. IEEE (2015)

    Google Scholar 

  42. Chhikara, R.R., Singh, L.: An improved discrete firefly and t-test based algorithm for blind image steganalysis. In: 2015 6th International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE (2015)

    Google Scholar 

  43. Su, H., Cai, Y., Qian, D.: Firefly-algorithm-inspired framework with band selection and extreme learning machine for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(1), 309–320 (2017)

    Article  Google Scholar 

  44. Polak, A., et al.: Hyperspectral imaging combined with data classification techniques as an aid for artwork authentication. J. Cult. Herit. 26, 1–11 (2017)

    Article  Google Scholar 

  45. Ramesh, R., Gomathy, C., Vaishali, D.: Bio inspired optimization for universal spatial image steganalysis. J. Comput. Sci. 21, 182–188 (2017)

    Article  Google Scholar 

  46. Chhikara, R.R., Singh, L.: A hybrid feature selection technique based on improved discrete firefly and filter approach for blind image steganalysis. Int. J. Simul. Syst., Sci. Technol. 16(4), 2–1, (2015)

    Google Scholar 

  47. Yang, L., et al.: Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification. Knowl.-Based Syst. 79, 80–89 (2015)

    Article  Google Scholar 

  48. Chhikara, R.R., Singh, L.: An improved discrete firefly and t-test based algorithm for blind image steganalysis. In: 2015 6th International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, 2015

    Google Scholar 

  49. Napoli, C., et al.: Toward 2D image classifier based on firefly algorithm with simplified sobel filter. In: 2015 Asia-Pacific Conference on Computer Aided System Engineering (APCASE). IEEE (2015)

    Google Scholar 

  50. Saberi, H., Rahai, A., Hatami, F.: A fast and efficient clustering based fuzzy time series algorithm (FEFTS) for regression and classification. Appl. Soft Comput. 61, 1088–1097 (2017)

    Article  Google Scholar 

  51. Aadit, M.N.A., Mahin, M.T., Juthi, S.N.: Spontaneous micro-expression recognition using optimal firefly algorithm coupled with ISO-FLANN classification. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE (2017)

    Google Scholar 

  52. Almonacid, B., et al.: Solving the manufacturing cell design problem using the modified binary firefly algorithm and the Egyptian vulture optimisation algorithm. IET Softw. 11(3), 105–115 (2016)

    Article  Google Scholar 

  53. Preethi, J., Sowmiya, S.: Emotion recognition from EEG signal using ISO-FLANN with firefly algorithm. In: 2016 International Conference on Communication and Signal Processing (ICCSP), IEEE (2016)

    Google Scholar 

  54. Muthuramalingam, A., Gnanamanickam, J., Muhammad, R.: Optimum feature selection using firefly algorithm for keystroke dynamics. In: International Conference on Intelligent Systems Design and Applications. Springer, Cham (2017)

    Google Scholar 

  55. Saraç, E., Ayşe Özel, S.: Web page classification using firefly optimization. In: 2013 IEEE International Symposium Innovations in Intelligent Systems and Applications (INISTA) (2013)

    Google Scholar 

  56. Sawhney, R., Mathur, P., Shankar, R.: A firefly algorithm based wrapper-penalty feature selection method for cancer diagnosis. In: International Conference on Computational Science and Its Applications. Springer, Cham (2018)

    Chapter  Google Scholar 

  57. Agarwal, V., Bhanot, S.: Radial basis function neural network-based face recognition using firefly algorithm. Neural Comput. Appl. 1–18 (2017)

    Google Scholar 

  58. Yang, X.S.: Review of meta-heuristics and generalized evolutionary walk algorithm. Int. J. Bio-Inspired Comput. 3(2), 77–84 (2011)

    Article  Google Scholar 

  59. Yang, X.S.: Metaheuristic optimization: algorithm analysis and open problems. In: Rebennack, P. (ed.) Experimental Algorithms, Lecture notes in Computer Science, vol. 6630, pp. 21–32. Springer, Berlin (2011)

    Google Scholar 

  60. Zamuda, A., Brest, J.: Population reduction differential evolution with multiple mutation strategies in real world industry challenges. In: ICAISC (SIDE-EC), pp. 154–161 (2012)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, 532410, Andhra Pradesh, India

    Janmenjoy Nayak, Kanithi Vakula & Paidi Dinesh

  2. Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Bighnaraj Naik

Authors
  1. Janmenjoy Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kanithi Vakula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paidi Dinesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bighnaraj Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bighnaraj Naik .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Soumen Kumar Pati

  5. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

Appendix

Appendix

ANN:

Artificial neural network

BPNN:

Back-propagation neural network

DFA:

Discrete firefly algorithm

DPSO:

Discrete particle swarm optimization

DCT:

Discrete cosine transformation

DWT:

Discrete wavelet transformation

ECG:

Electrocardiogram

EEG:

Electro encephalogram

ELM:

Extreme learning machine

FA:

Firefly algorithm

FEFTS:

Fast and efficient clustering-based fuzzy time series

FLANN:

Functional link artificial neural network

FLNN:

Functional link neural network

FTS:

Fuzzy time series

ISO-FLANN:

Improved swarm optimized-functional link artificial neural network

KNN:

k-nearest neighbor

LFA:

Levy flight firefly algorithm

LSFA:

Levy flight integrated simulated annealing firefly algorithm

MCDP:

Manufacturing cell design problem

MLNN:

Multilayered associative neural networks

NN:

Neural network

PSO:

Particle swarm optimization

SVM:

Support vector machine

VER:

Virtualized-elastic-regenerator

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nayak, J., Vakula, K., Dinesh, P., Naik, B. (2020). Applications and Advancements of Firefly Algorithm in Classification: An Analytical Perspective. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_87

Download citation

Publish with us

Policies and ethics

Search

Navigation