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A K-means Grasshopper Algorithm Applied to the Knapsack 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

In engineering and science, there are many combinatorial optimization problems. A lot of these problems are NP-hard and can hardly be addressed by full techniques. Therefore, designing binary algorithms based on swarm intelligence continuous metaheuristics is an area of interest in operational research. In this paper we use a general binarization mechanism based on the k-means technique. We apply the k-means technique to grasshopper algorithm to solve multidimensional knapsack problem (MKP). Experiments are designed to demonstrate the utility of the k-means technique in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that binary k-means grasshopper algorithm (BKGOA) obtains adequate results when it is evaluated against another state of the art algorithm.

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Author information

Authors and Affiliations

  1. Escuela de ingeniería en construcción, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Hernan Pinto, Alvaro Peña, Matías Valenzuela & Gabriel Villavicencio

  2. Escuela de Tecnologías de la Información, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago, Chile

    Leonardo Causa

Authors
  1. Hernan Pinto
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  2. Alvaro Peña
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  3. Leonardo Causa
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  4. Matías Valenzuela
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  5. Gabriel Villavicencio
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Corresponding author

Correspondence to Gabriel Villavicencio .

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Editors and Affiliations

  1. Tomas Bata University, Zlín, Czech Republic

    Radek Silhavy

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Pinto, H., Peña, A., Causa, L., Valenzuela, M., Villavicencio, G. (2020). A K-means Grasshopper Algorithm Applied to the Knapsack 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_19

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