A binary grasshopper algorithm applied to the knapsack problem

Hernan Pinto, Alvaro Peña, Matías Valenzuela, Andrés Fernández

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

10 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence and Algorithms in Intelligent Systems - Proceedings of 7th Computer Science On-line Conference, 2018
EditoresRadek Silhavy
EditorialSpringer Verlag
Páginas132-143
Número de páginas12
ISBN (versión impresa)9783319911885
DOI
EstadoPublicada - 2019
Evento7th Computer Science On-line Conference, CSOC 2018 - Zlin, República Checa
Duración: 25 abr. 201828 abr. 2018

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen764
ISSN (versión impresa)2194-5357

Conferencia

Conferencia7th Computer Science On-line Conference, CSOC 2018
País/TerritorioRepública Checa
CiudadZlin
Período25/04/1828/04/18

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