A Binary Bat Algorithm Applied to Knapsack Problem

Lorena Jorquera, Gabriel Villavicencio, Leonardo Causa, Luis Lopez, Andrés Fernández

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Combinatorial problems with NP-hard complexity appear frequently in operational research. Making robust algorithms that solve these combinatorial problems is of interest in operational research. In this article, a binarization mechanism is proposed so that continuous metaheuristics can solve combinatorial problems. The binarization mechanism uses the concept of percentile. This percentile mechanism is applied to the bat algorithm. The NP-hard knapsack problem (MKP) was used to verify our algorithm. Additionally, the binary percentile algorithm was compared with other algorithms that have recently has solved the MKP, observing that the percentile algorithm produces competitive results.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence and Bioinspired Computational Methods - Proceedings of the 9th Computer Science On-line Conference, CSOC 2020
EditoresRadek Silhavy
EditorialSpringer
Páginas172-182
Número de páginas11
ISBN (versión impresa)9783030519704
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento9th Computer Science On-line Conference, CSOC 2020 - Zlin, República Checa
Duración: 15 jul. 202015 jul. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1225 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia9th Computer Science On-line Conference, CSOC 2020
País/TerritorioRepública Checa
CiudadZlin
Período15/07/2015/07/20

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