A Machine Learning Whale Algorithm Applied to the Matrix Covering Problem

Matias Valenzuela, Paola Moraga, Leonardo Causa, Hernan Pinto, José Miguel Rubio

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

Abstract

In the industry, the need for optimization naturally arises, with which there is a large number of optimization problems, particularly combinatorial and NP-hard type. Therefore, there is an important motivation for the development of algorithms that address these types of problems. IN this line, a large number of metaheuristic algorithms have been developed which work only in continuous spaces. Modifying the latter in order to address combinatorial problems has important applications. In this article, we will study a general binarization mechanism of continuous metaheuristics based on clustering techniques and it will be applied to the whale algorithm. Experiments are designed in order to demonstrate the contribution of the clustering technique in the binarization process. The results indicate that the Ballena binary optimization algorithm (MLWH) obtains adequate results when it is evaluated with a combinatorial problem such as the SCP.

Original languageEnglish
Title of host publicationData Science and Intelligent Systems - Proceedings of 5th Computational Methods in Systems and Software 2021
EditorsRadek Silhavy, Petr Silhavy, Zdenka Prokopova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages413-422
Number of pages10
ISBN (Print)9783030903206
DOIs
StatePublished - 2021
Externally publishedYes
Event5th Computational Methods in Systems and Software, CoMeSySo 2021 - Virtual, Online
Duration: 1 Oct 20211 Oct 2021

Publication series

NameLecture Notes in Networks and Systems
Volume231 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference5th Computational Methods in Systems and Software, CoMeSySo 2021
CityVirtual, Online
Period1/10/211/10/21

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