A binary grasshopper algorithm applied to the knapsack problem

Hernan Pinto, Alvaro Peña, Matías Valenzuela, Andrés Fernández

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


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.

Original languageEnglish
Title of host publicationArtificial Intelligence and Algorithms in Intelligent Systems - Proceedings of 7th Computer Science On-line Conference, 2018
EditorsRadek Silhavy
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319911885
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


  • Combinatorial optimization
  • KnapSack
  • Metaheuristics
  • Percentile


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