A Binary Bat Algorithm Applied to Knapsack Problem

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

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

2 Scopus citations


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.

Original languageEnglish
Title of host publicationArtificial Intelligence and Bioinspired Computational Methods - Proceedings of the 9th Computer Science On-line Conference, CSOC 2020
EditorsRadek Silhavy
Number of pages11
ISBN (Print)9783030519704
StatePublished - 2020
Event9th Computer Science On-line Conference, CSOC 2020 - Zlin, Czech Republic
Duration: 15 Jul 202015 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1225 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference9th Computer Science On-line Conference, CSOC 2020
Country/TerritoryCzech Republic


  • Combinatorial optimization
  • Metaheuristics
  • Multidimensional knapsack problem


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