A K-means Bat Optimisation Algorithm Applied to the Set Covering Problem

Leonardo Pavez, Francisco Altimiras, Gabriel Villavicencio

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

1 Scopus citations


Optimization at the industry level is a fundamental area since it allows reducing costs and being more sustainable. Many of these problems are combinatorial and NP-hard. On the other hand, swarm intelligence metaheuristics have been able to successfully address these types of problems, however, many of these techniques work in searching continuous spaces. In this article, we explore a general binarization mechanism of continuous metaheuristics based on the k-means technique. In particular, we applied the k-means technique to the bat algorithm with the aim of addressing the set covering problem (SCP). Experiments were designed to evaluate the contribution of the k-means technique in the binarization process. In addition, we verify the effectiveness of our algorithm through reference instances. The results indicate that the k-means binary bat algorithm (KBBA) gets adequate results when evaluated with a combinatorial problem like SCP.

Original languageEnglish
Title of host publicationSoftware Engineering Perspectives in Intelligent Systems - Proceedings of 4th Computational Methods in Systems and Software 2020
EditorsRadek Silhavy, Petr Silhavy, Zdenka Prokopova
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030633189
StatePublished - 2020
Event4th Computational Methods in Systems and Software, CoMeSySo 2020 - Vsetin, Czech Republic
Duration: 14 Oct 202017 Oct 2020

Publication series

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


Conference4th Computational Methods in Systems and Software, CoMeSySo 2020
Country/TerritoryCzech Republic


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