Abstract
Areas of swarm intelligence and machine learning are constantly evolving, recently attracting even more researchers world-wide. This stems from the no free lunch which states that universal approach that could render satisfying results for all practical challenges does not exist. Therefore, in this research a novel multi-swarm firefly algorithm, that tries to address flaws of original firefly metaheuristics, is proposed. Devised algorithm is applied to interesting and important practical challenge of plants classification, as part of the hybrid framework between machine learning and optimization metaheuristics. For this purpose, a set of 1,000 random images of healthy leaves, from one public repository, is retrieved for the following plants: apple, cherry, pepper and tomato. Hybrid framework includes pre-processing, constructing bag of features and classification steps. After pre-processing, a bag of features is constructed by utilizing well-known scale-invariant feature transform algorithm, K-means-based vocabulary generation and histogram. Such images are then categorized with support vector machine classifier. However, to obtain satisfying results for a particular dataset, the support vector machines hyper-parameters’ need to be tuned and in the proposed research multi-swarm firefly algorithm is employed to determine optimal (sub-optimal) hyper-parameters’ values for this practical challenge. Comparative analysis with the basic firefly metaheuristics and other well-known swarm intelligence algorithms was conducted to assess the performance of the proposed method in terms of precision, recall, F-score for this multi-class classification challenge. Obtained results show significant performance improvements of devised method over the original firefly algorithm and also better metrics than other state-of-the-art techniques in the majority of cases.
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References
Altameem, A., Kumar, S., Poonia, R., Saudagar, A.: Plant identification using fitness-based position update in whale optimization algorithm. Comput. Mater. Continua 71, 4719–4736 (2022). https://doi.org/10.32604/cmc.2022.022177
Azhar, R., Tuwohingide, D., Kamudi, D., Suciati, N., et al.: Batik image classification using sift feature extraction, bag of features and support vector machine. Procedia Comput. Sci. 72, 24–30 (2015)
Bacanin, N., Alhazmi, K., Zivkovic, M., Venkatachalam, K., Bezdan, T., Nebhen, J.: Training multi-layer perceptron with enhanced brain storm optimization metaheuristics. Comput. Mater. Continua 70(2), 4199–4215 (2022)
Bacanin, N., Alhazmi, K., Zivkovic, M., Venkatachalam, K., Bezdan, T., Nebhen, J.: Training multi-layer perceptron with enhanced brain storm optimization metaheuristics. Comput. Mater. Continua 70(2), 4199–4215 (2022). https://doi.org/10.32604/cmc.2022.020449,http://www.techscience.com/cmc/v70n2/44706
Bacanin, N., Bezdan, T., Venkatachalam, K., Al-Turjman, F.: Optimized convolutional neural network by firefly algorithm for magnetic resonance image classification of glioma brain tumor grade. J. Real-Time Image Proc. 18(4), 1085–1098 (2021). https://doi.org/10.1007/s11554-021-01106-x
Bacanin, N., et al.: Artificial neural networks hidden unit and weight connection optimization by quasi-refection-based learning artificial bee colony algorithm. IEEE Access (2021)
Bacanin, N., Bezdan, T., Zivkovic, M., Chhabra, A.: Weight optimization in artificial neural network training by improved monarch butterfly algorithm. In: Shakya, S., Bestak, R., Palanisamy, R., Kamel, K.A. (eds.) Mobile Computing and Sustainable Informatics. LNDECT, vol. 68, pp. 397–409. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-1866-6_29
Bacanin, N., Petrovic, A., Zivkovic, M., Bezdan, T., Antonijevic, M.: Feature selection in machine learning by hybrid sine cosine metaheuristics. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds.) ICACDS 2021. CCIS, vol. 1440, pp. 604–616. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-81462-5_53
Bacanin, N., Sarac, M., Budimirovic, N., Zivkovic, M., AlZubi, A.A., Bashir, A.K.: Smart wireless health care system using graph lstm pollution prediction and dragonfly node localization. Sustain. Comput. Inf. Syst. 35, 100711 (2022)
Bacanin, N., Stoean, R., Zivkovic, M., Petrovic, A., Rashid, T.A., Bezdan, T.: Performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: application for dropout regularization. Mathematics 9(21), 1–33 (2021). https://doi.org/10.3390/math9212705,https://www.mdpi.com/2227-7390/9/21/2705
Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. 2014, 721521 (2014)
Bacanin, N., Vukobrat, N., Zivkovic, M., Bezdan, T., Strumberger, I.: Improved harris hawks optimization adapted for artificial neural network training. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 308, pp. 281–289. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85577-2_33
Bacanin, N., Zivkovic, M., Bezdan, T., Venkatachalam, K., Abouhawwash, M.: Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Comput. Appli. 1–26 (2022)
Bezdan, T., Cvetnic, D., Gajic, L., Zivkovic, M., Strumberger, I., Bacanin, N.: Feature selection by firefly algorithm with improved initialization strategy. In: 7th Conference on the Engineering of Computer Based Systems, pp. 1–8 (2021)
Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Glioma brain tumor grade classification from MRI using convolutional neural networks designed by modified FA. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 955–963. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_111
Chen, H.L., Yang, B., Wang, G., Wang, S.J., Liu, J., Liu, D.Y.: Support vector machine based diagnostic system for breast cancer using swarm intelligence. J. Med. Syst. 36(4), 2505–2519 (2012)
Chen, J., Chen, J., Zhang, D., Sun, Y., Nanehkaran, Y.A.: Using deep transfer learning for image-based plant disease identification. Comput. Electron. Agric. 173, 105393 (2020)
Hughes, D.P., Salath’e, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing (2015). CoRR abs/1511.08060, http://arxiv.org/abs/1511.08060
Kaur, P., Gautam, R., Sharma, M.: Feature selection for bi-objective stress classification using emerging swarm intelligence metaheuristic techniques. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds.) Proceedings of Data Analytics and Management. LNDECT, vol. 91, pp. 357–365. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-6285-0_29
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Lv, S., Song, F.: Particle swarm intelligence and the evolution of cooperation in the spatial public goods game with punishment. Appl. Math. Comput. 412, 126586 (2022)
Mirjalili, S.: Sca: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)
Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Rzanny, M., Seeland, M., Wäldchen, J., Mäder, P.: Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods 13(1), 1–11 (2017)
Storn, R.: On the usage of differential evolution for function optimization. In: Proceedings of north american fuzzy information processing, pp. 519–523. IEEE (1996)
Strumberger, I., Tuba, E., Bacanin, N., Zivkovic, M., Beko, M., Tuba, M.: Designing convolutional neural network architecture by the firefly algorithm. In: 2019 International Young Engineers Forum, YEF-ECE, pp. 59–65. IEEE (2019)
Wang, M., Chen, H.: Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. 88, 105946 (2020)
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
Yang, X.S., Hossein Gandomi, A.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)
Zivkovic, M., Bacanin, N., Tuba, E., Strumberger, I., Bezdan, T., Tuba, M.: Wireless sensor networks life time optimization based on the improved firefly algorithm. In: 2020 International Wireless Communications and Mobile Computing, IWCMC, pp. 1176–1181. IEEE (2020)
Zivkovic, M., et al.: Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Urban Areas 66, 102669 (2021)
Zivkovic, M., Stoean, C., Chhabra, A., Budimirovic, N., Petrovic, A., Bacanin, N.: Novel improved salp swarm algorithm: an application for feature selection. Sensors 22(5), 1711 (2022)
Zivkovic, M., K, V., Bacanin, N., Djordjevic, A., Antonijevic, M., Strumberger, I., Rashid, T.A.: Hybrid genetic algorithm and machine learning method for COVID-19 cases prediction. In: Shakya, S., Balas, V.E., Haoxiang, W., Baig, Z. (eds.) Proceedings of International Conference on Sustainable Expert Systems. LNNS, vol. 176, pp. 169–184. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4355-9_14
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The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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Bacanin, N. et al. (2022). A Novel Multiswarm Firefly Algorithm: An Application for Plant Classification. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-09173-5_115
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