<?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>1409-2433</journal-id>
<journal-title><![CDATA[Revista de Matemática Teoría y Aplicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Mat]]></abbrev-journal-title>
<issn>1409-2433</issn>
<publisher>
<publisher-name><![CDATA[Centro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1409-24332023000200173</article-id>
<article-id pub-id-type="doi">10.15517/rmta.v30i2.51162</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Optimizando con búsqueda tabú en particionamiento sobre datos espaciales con múltiples objetivos]]></article-title>
<article-title xml:lang="en"><![CDATA[Optimization with tabu search in spatial data clustering with multiple objectives]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bernábe Loranca]]></surname>
<given-names><![CDATA[Maria Beatríz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez Flores]]></surname>
<given-names><![CDATA[Marco Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Garnica]]></surname>
<given-names><![CDATA[Carmen Cerón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Guzmán]]></surname>
<given-names><![CDATA[Gerardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Benemérita Universidad Autónoma de Puebla Facultad de Ciencias de la Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Cyprus University of Technology  Department of Electrical Engineering and Computer Engineering and Informatics]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Chipre</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Benemérita Universidad Autónoma de Puebla Facultad de Ciencias de la Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Benemérita Universidad Autónoma de Puebla Facultad de Ciencias de la Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>30</volume>
<numero>2</numero>
<fpage>173</fpage>
<lpage>192</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1409-24332023000200173&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1409-24332023000200173&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1409-24332023000200173&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El particionamiento sobre datos geográficos es de gran utilidad para resolver problemas relacionados con diseño territorial. Para instancias de tamaño pequeño, este problema incluso es resuelto por métodos exactos en un tiempo de respuesta aceptable. Sin embargo, para instancias de tamaño grande y debido a la naturaleza combinatoria de este problema, la complejidad computacional aumenta y el uso de métodos de aproximación se ha hecho necesario. Un caso en particular de este tipo de problemas que ha tenido nuestra atención en los últimos a&#732;nos es el agrupamiento por particiones para AGEBS (áreas geoestadísticas básicas). Algunos trabajos relacionados se han desarrollado para resolver la formación de grupos compactos de AGEBS, pero la incorporación de restricciones adicionales ha sido poco tratada. Un problema interesante de aplicación muy demandado, es la extensión del agrupamiento compacto para construir grupos bajo el criterio de homogeneidad y/o balanceo en el número de objetos que componen los grupos. Este problema se traduce en un problema multiobjetivo, el cual debe lidiar con dos objetivos para conseguir un compromiso entre ambos. Este trabajo presenta un modelo de programación matemática multiobjetivo y su asociada implementación para lograr el equilibrio entre compacidad y homogeneidad en la cardinalidad de objetos. La metaheurística incorporada a este problema de agrupamiento territorial multiobjetivo ha sido búsqueda tabú.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Clustering spatial-geographic units, zones or areas is employed to solve problems related to territorial design. The clustering adapts to the definition of territorial design of a particular problem, which demands spatial data processing under clustering schemes with topological requirements in the zones. For small instances, when geographical compactness is attended as an objective function, this problem is solved by exact methods in an aceptable response time. However, for bigger instances and due to the combinatory nature of this problem, the computational complexity increases and the employment of approximated methods becomes a necessity, in such a way that when the geographical compactness was the only cost function, a couple of approximated methods were implemented, giving satisfactory results. A particular case of this kind of problems that has our attention in recent years is the classification of AGEBS (basic geographical units by its initials in Spanish) through partitions. Some works were made related to the formation of compact groups of AGEBS, but additional restrictions were not often considered. A very interesting and demanded application problem is extending the compact clustering to form groups under a homogeneity criterion to balance the number of objects in every group. This problem implies a multiobjective approach that has to tackle two objectives to attain a balance between the two. This work presents a mathematical model and the resulting implementation to achieve the equilibrium between compactness and homogeneity in the number of objects. The metaheuristic incorporated to this multiobjective clustering problem is tabu search.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[particionamiento]]></kwd>
<kwd lng="es"><![CDATA[multiobjetivo]]></kwd>
<kwd lng="es"><![CDATA[búsqueda tabú]]></kwd>
<kwd lng="es"><![CDATA[diseño territorial]]></kwd>
<kwd lng="es"><![CDATA[metaheurísticas.]]></kwd>
<kwd lng="en"><![CDATA[partitioning, multiobjective]]></kwd>
<kwd lng="en"><![CDATA[tabu search]]></kwd>
<kwd lng="en"><![CDATA[territorial design]]></kwd>
<kwd lng="en"><![CDATA[metaheuristics.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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