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A Binary Grasshopper Algorithm Applied to the Knapsack Problem

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 764)

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 percentile concept. We apply the percentile concept to grasshopper algorithm to solve multidimensional knapsack problem (MKP). Experiments are designed to demonstrate the utility of the percentile concept in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that binary grasshopper algorithm (BGOA) obtains adequate results when it is evaluated against another state of the art algorithm.

Keywords

  • Combinatorial optimization
  • KnapSack
  • Metaheuristics
  • Percentile

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Correspondence to Alvaro Peña .

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Pinto, H., Peña, A., Valenzuela, M., Fernández, A. (2019). A Binary Grasshopper Algorithm Applied to the Knapsack Problem. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_14

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