Abstract
Heuristics and metaheuristics are known to be sensitive to input parameters. Bat algorithm (BA), a recent optimization metaheuristic, has a great number of input parameters that need to be adjusted in order to increase the quality of the results. Despites the crescent number of works with BA in literature, to the best of our knowledge, there is no work that aims the fine tuning of the parameters. In this work we use benchmark functions and more than 9 millions tests with BA in order to find the best set of parameters. Our experiments shown that we can have almost 14000% of difference in objective function value between the best and the worst set of parameters. Finally, this work shows how to choose input parameters in order to make Bat Algorithm to achieve better results.
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The authors thank CNPq, FAPEMIG and CAPES for the financial support.
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Carvalho, I.A., da Rocha, D.G., Silva, J.G.R., da Fonseca Vieira, V., Xavier, C.R. (2017). Study of Parameter Sensitivity on Bat Algorithm. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_36
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DOI: https://doi.org/10.1007/978-3-319-62392-4_36
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