Abstract
Bio-inspired optimization algorithms aim to address the most diverse problems without the need for derivatives, and they are independent of the shape of the search space. The Flying Squirrel Optimizer belongs to the family of bio-inspired algorithms and simulates the movement of flying squirrels from tree to tree in search of food. This paper proposes a binary version of the flying squirrel optimizer for feature selection problems. To elucidate the performance of the proposed algorithm, we employed six other well-known bio-inspired algorithms for comparison purposes in sixteen benchmark datasets widely known in the literature. Furthermore, we employ the binary flying squirrel optimizer in selecting gas concentrations to identify faults in power transformers. The results expressed that Binary Flying Squirrell Optimizer can either find compact feature sets or improve classification effectiveness, corroborating its robustness.
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Notes
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It is worthy to say that any other supervised classifier can be used. We recommend models that figure a reasonably efficient training step, for the fitness function might be evaluated several times during the optimization process.
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The algorithms used for comparison purposes and FSO are part of Opytimizer library, which contains several implementations of metaheuristics in Python. The Opytimizer library is available in: https://github.com/gugarosa/opytimizer.
References
IEC 60599:2022 Mineral oil-filled electrical equipment in service - Guidance on the interpretation of dissolved and free gases analysis. IEC, Geneva, Switzerland, 4 edn. (2022)
Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23(3), 715–734 (2019). https://doi.org/10.1007/s00500-018-3102-4
Azizyan, G., Miarnaeimi, F., Rashki, M., Shabakhty, N.: Flying squirrel optimizer (FSO): A novel SI-based optimization algorithm for engineering problems. Iranian J. Optimiz. 11(2), 177–205 (2019)
Chou, J.S., Truong, D.N.: A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl. Math. Comput. 389, 125535 (2021)
Equbal, M.D., Khan, S.A., Islam, T.: Transformer incipient fault diagnosis on the basis of energy-weighted dga using an artificial neural network. Turk. J. Electr. Eng. Comput. Sci. 26(1), 77–88 (2018)
Falcón, R., Almeida, M., Nayak, A.: Fault identification with binary adaptive fireflies in parallel and distributed systems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1359–1366. IEEE (2011)
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108 (1997)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Li, L., Pan, J.S., Zhuang, Z., Chu, S.C.: A novel feature selection algorithm based on aquila optimizer for covid-19 classification. In: Shi, Z., Zucker, J.D., An, B. (eds.) Intelligent Information Processing XI, pp. 30–41. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-03948-5_3
Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of lévy stable stochastic processes. Phys. Rev. E 49, 4677–4683 (1994)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008
Nakamura, R.Y.M., Pereira, L.A.M., Costa, K.A., Rodrigues, D., Papa, J.P., Yang, X.S.: BBA: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 291–297 (2012)
Nemenyi, P.: Distribution-free Multiple Comparisons. Princeton University (1963)
Rodrigues, D., et al.: BCS: a binary cuckoo search algorithm for feature selection. In: IEEE International Symposium on Circuits and Systems, pp. 465–468 (2013)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)
Yang, X.S.: Flower pollination algorithm for global optimization. In: International conference on Unconventional Computing and Natural Computation, pp. 240–249. Springer (2012). https://doi.org/10.1007/978-3-031-03948-5_3
Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. (2012)
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de Oliveira Sementille, L.F.M., Rodrigues, D., de Souuza, A.N., Papa, J.P. (2023). Binary Flying Squirrel Optimizer for Feature Selection. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_4
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