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.
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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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