A binary grasshopper optimisation algorithm applied to the set covering problem

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8 Scopus citations


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.

Original languageEnglish
Title of host publicationSoftware Engineering and Algorithms in Intelligent Systems - Proceedings of 7th Computer Science On-line Conference 2018
EditorsRadek Silhavy
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319911915
StatePublished - 2019
Event7th Computer Science On-line Conference, CSOC 2018 - Zlin, Czech Republic
Duration: 25 Apr 201828 Apr 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Conference7th Computer Science On-line Conference, CSOC 2018
Country/TerritoryCzech Republic


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