A Binary Grasshopper Optimisation Algorithm Applied to the Set Covering Problem

  • Broderick Crawford
  • Ricardo Soto
  • Alvaro Peña
  • Gino Astorga
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 765)


Many of the problems addressed at the industrial level are of a combinatorial type and a sub-assembly not less than these are of the NP-hard type. The design of algorithms that solve combinatorial problems based on the continuous metaheuristic of swarm intelligence is an area of interest at an industrial level. In this article, we explore a general binarization mechanism of continuous metaheuristics based on the percentile concept. In particular, we apply the percentile concept to the Grasshopper optimization algorithm in order to solve the set covering problem (SCP). The experiments are designed with the aim of demonstrating the usefulness of the percentile concept in binarization. Additionally, we verify the effectiveness of our algorithm through reference instances. The results indicate the binary grasshopper optimization algorithm (BGOA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Broderick Crawford
    • 1
  • Ricardo Soto
    • 1
  • Alvaro Peña
    • 1
  • Gino Astorga
    • 1
  1. 1.School of EngineeringPontificia Universidad Católica de ValparaísoValparaísoChile

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