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A Percentil Bat Algorithm an Application to the Set Covering Problem

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Artificial Intelligence and Bioinspired Computational Methods (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1225))

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Abstract

The study and understanding of algorithms that solve combinatorial problems based on swarm intelligence continuous metaheuristics, is an area of interest at the level of basic and applied science. This is due to the fact that many of the problems addressed at industrial level are of a combinatorial type and a subset no less than these are of the NP-hard type. In this article, a mechanism of binarization of continuous metaheuristics that uses the concept of the percentile is proposed. This percentile concept is applied to the An Lion optimization algorithm, solving the set covering problem (SCP). Experiments were designed to demonstrate the importance of the percentile concept in the binarization process. Subsequently, the efficiency of the algorithm is verified through reference instances. The results indicate that the binary percentile bat Algorithm (BPBA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.

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Authors and Affiliations

  1. Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Lorena Jorquera, Pamela Valenzuela, Paola Moraga & Gabriel Villavicencio

  2. Facultad de Ingeniería y Negocios, Universidad de le Américas, Santiago, Chile

    Francisco Altimiras

Authors
  1. Lorena Jorquera
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  2. Pamela Valenzuela
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  3. Francisco Altimiras
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  4. Paola Moraga
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  5. Gabriel Villavicencio
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Corresponding author

Correspondence to Lorena Jorquera .

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Editors and Affiliations

  1. Tomas Bata University, Zlín, Czech Republic

    Radek Silhavy

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Jorquera, L., Valenzuela, P., Altimiras, F., Moraga, P., Villavicencio, G. (2020). A Percentil Bat Algorithm an Application to the Set Covering Problem. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_18

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