A Machine Learning Firefly Algorithm Applied to the Matrix Covering Problem

Gabriel Villavicencio, Matias Valenzuela, Leonardo Causa, Paola Moraga, Hernan Pinto

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

1 Scopus citations

Abstract

At the business level, there are a large number of combinatorial problems. A subset of these is NP-hard type. The study of algorithms that address this type of problem is of great interest. On the other hand, there are a large number of metaheuristic algorithms that naturally work in continuous spaces. Adapting the latter to solve combinatorial problems is of great interest at an industrial level. In this article, we explore a general binarization mechanism of continuous metaheuristics based on cauterization techniques. The experiments are designed to demonstrate the utility of the clustering technique in binarization. Besides, we verify the effectiveness of our algorithm through reference instances. The results indicate that the binary firefly optimization algorithm (MLFA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.

Original languageEnglish
Title of host publicationArtificial Intelligence in Intelligent Systems - Proceedings of 10th Computer Science On-line Conference, 2021
EditorsRadek Silhavy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages316-325
Number of pages10
ISBN (Print)9783030774448
DOIs
StatePublished - 2021
Externally publishedYes
Event10th Computer Science Online Conference, CSOC 2021 - Virtual, Online
Duration: 1 Apr 20211 Apr 2021

Publication series

NameLecture Notes in Networks and Systems
Volume229
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference10th Computer Science Online Conference, CSOC 2021
CityVirtual, Online
Period1/04/211/04/21

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