<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-1129</journal-id>
<journal-title><![CDATA[Revista Facultad de Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Fac. ing.]]></abbrev-journal-title>
<issn>0121-1129</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-11292023000100006</article-id>
<article-id pub-id-type="doi">10.19053/01211129.v32.n63.2023.15235</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Initialization and Local Search Methods Applied to the Set Covering Problem: A Systematic Mapping]]></article-title>
<article-title xml:lang="es"><![CDATA[Métodos de inicialización y búsqueda local aplicados al problema de cobertura de conjuntos: un mapeo sistemático]]></article-title>
<article-title xml:lang="pt"><![CDATA[Métodos de Inicialização e Pesquisa Local Aplicados ao Problema de Cobertura de Conjuntos: Um Mapeamento Sistemático]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quemá-Taimbud]]></surname>
<given-names><![CDATA[Nelson-Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mendoza-Becerra]]></surname>
<given-names><![CDATA[Martha-Eliana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bedoya-Leyva]]></surname>
<given-names><![CDATA[Oscar-Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán Cauca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán Cauca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali Valle del Cauca]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>32</volume>
<numero>63</numero>
<fpage>6</fpage>
<lpage>6</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-11292023000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-11292023000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-11292023000100006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The set covering problem (SCP) is a classical combinatorial optimization problem part of Karp's 21 NP-complete problems. Many real-world applications can be modeled as set covering problems (SCPs), such as locating emergency services, military planning, and decision-making in a COVID-19 pandemic context. Among the approaches that this type of problem has solved are heuristic (H) and metaheuristic (MH) algorithms, which integrate iterative methods and procedures to explore and exploit the search space intelligently. In the present research, we carry out a systematic mapping of the literature focused on the initialization and local search methods used in these algorithms that have been applied to the SCP in order to identify them and that they can be applied in other algorithms. This mapping was carried out in three main stages: research planning, implementation, and documentation of results. The results indicate that the most used initialization method is random with heuristic search, and the inclusion of local search methods in MH algorithms improves the results obtained in comparison to those without local search. Moreover, initialization and local search methods can be used to modify other algorithms and evaluate the impact they generate on the results obtained.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El problema de cobertura de conjuntos (PCC) es un problema de optimización combinatorio clásico que hace parte de los 21 problemas NP-completos de Karp. Muchas aplicaciones del mundo real pueden modelarse como PCC, como la ubicación servicios de emergencia, la planificación militar, la toma de decisiones en el contexto de la pandemia por COVID-19, entre otros. Entre los enfoques que han resuelto este tipo de problema se encuentran los algoritmos heurísticos (H) y metaheurísticos (MH), que integran métodos y procedimientos iterativos para explorar y explotar el espacio de búsqueda de forma inteligente. En la presente investigación realizamos un mapeo sistemático de la literatura enfocado en los métodos de inicialización y búsqueda local utilizados en estos algoritmos que se han aplicado al PCC con el fin de identificarlos y que puedan ser aplicados en otros algoritmos. Este mapeo se realizó en tres etapas principales: planificación de la investigación, ejecución y documentación de resultados. Se encontró que el método de inicialización más utilizado es el aleatorio con búsqueda heurística y que la inclusión de métodos de búsqueda local en los algoritmos MH mejoran los resultados obtenidos en comparación con estos sin búsqueda local. Por otra parte, se identificaron métodos de inicialización y búsqueda local que pueden ser utilizados para modificar otros algoritmos y evaluar el impacto que generan en los resultados obtenidos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo O problema de cobertura de conjuntos (PCC) é um problema de otimização de combinação clássica que faz parte dos 21 problemas de Karp completados por NP. Muitas aplicações do mundo real podem ser modeladas como PCC, como serviços de emergência, planejamento militar, tomada de decisão pandêmica pelo Covid-19, entre outros. Entre as abordagens que têm resolvido esse tipo de problema estão os algoritmos heurísticos (H) e metaheurísticos (MH), que integram métodos e procedimentos iterativos para intensificar ou diversificar a pesquisa de forma inteligente. Na presente investigação, realizamos um mapeamento sistemático da literatura focada na inicialização e nos métodos de pesquisa local usados nesses algoritmos que foram aplicados ao PCC para identificá-los e que podem ser aplicados em outros algoritmos. Esse mapeamento foi realizado em três etapas principais: planejamento de pesquisa, execução e documentação dos resultados; Descobrir que o método de inicialização mais usado é aleatório com a pesquisa heurística e que a inclusão de métodos de pesquisa local nos algoritmos MH melhora os resultados obtidos em comparação com eles sem a pesquisa local. Por outro lado, foram identificados métodos de localização e pesquisa local que podem ser usados para modificar outros algoritmos e avaliar o impacto que eles geram nos resultados obtidos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[heuristics]]></kwd>
<kwd lng="en"><![CDATA[initialization]]></kwd>
<kwd lng="en"><![CDATA[local search]]></kwd>
<kwd lng="en"><![CDATA[metaheuristics]]></kwd>
<kwd lng="en"><![CDATA[optimization]]></kwd>
<kwd lng="en"><![CDATA[set covering problem]]></kwd>
<kwd lng="en"><![CDATA[systematic mapping]]></kwd>
<kwd lng="es"><![CDATA[búsqueda local]]></kwd>
<kwd lng="es"><![CDATA[heurísticas]]></kwd>
<kwd lng="es"><![CDATA[inicialización]]></kwd>
<kwd lng="es"><![CDATA[mapeo sistemático]]></kwd>
<kwd lng="es"><![CDATA[metaheurísticas]]></kwd>
<kwd lng="es"><![CDATA[optimización]]></kwd>
<kwd lng="es"><![CDATA[problema de cobertura de conjuntos]]></kwd>
<kwd lng="pt"><![CDATA[heurística]]></kwd>
<kwd lng="pt"><![CDATA[inicialização]]></kwd>
<kwd lng="pt"><![CDATA[mapeamento sistemático]]></kwd>
<kwd lng="pt"><![CDATA[metaheurística]]></kwd>
<kwd lng="pt"><![CDATA[otimização]]></kwd>
<kwd lng="pt"><![CDATA[pesquisa local]]></kwd>
<kwd lng="pt"><![CDATA[problema de cobertura de conjuntos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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