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Optimization of non-stationary Stackelberg models using a self-adaptive evolutionary algorithm

Optimización de modelos de Stackelberg no estacionarios mediante un algoritmo evolutivo auto-adaptativo

dc.creatorCedeño-Fuentes, Olga P.
dc.creatorArboleda-Castro, Lorena
dc.creatorJacho-Sánchez, Iván
dc.creatorNovoa-Hernández, Pavel
dc.date2017-05-02
dc.date.accessioned2021-03-18T21:06:50Z
dc.date.available2021-03-18T21:06:50Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/715
dc.identifier10.22430/22565337.715
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11708
dc.descriptionStackelberg’s game models involve an important family of Game Theory problems with direct application on economics scenarios. Their main goal is to find an optimal equilibrium between the decisions from two actors that are related one to each other hierarchically. In general, these models are complex to solve due to their hierarchical structure and intractability from an analytical viewpoint. Another reason for such a complexity comes from the presence of uncertainty, which often occurs because of the variability over time of market conditions, adversary strategies, among others aspects. Despite their importance, related literature reflects a few works addressing this kind of non-stationary optimization problems. So, in order to contribute to this research area, the present work proposes a self-adaptive meta-heuristic method for solving online Stackelberg’s games. Experiment results show a significant improvement over an existing method.en-US
dc.descriptionLos modelos de Juegos de Stackelberg engloban una importante familia de problemas de la Teoría de Juegos, que encuentra aplicaciones directas en economía. El principal objetivo es encontrar un equilibrio óptimo entre las decisiones que pueden tomar dos actores que se relacionan jerárquicamente. En general estos modelos son complejos de resolver dada su estructura jerárquica, y la frecuente aparición en estos de funciones objetivos o restricciones intratables analíticamente. Otra causa de dicha complejidad es la existencia de incertidumbre, particularmente debido a la variabilidad en el tiempo de las condiciones del mercado, estrategias de los competidores, entre otras. Un análisis de la literatura relacionada muestra muy pocos trabajos abordando estos problemas de optimización no estacionarios. En este sentido, la presente investigación propone una técnica meta-heurística auto-adaptativa para resolver modelos de Juegos de Stackelberg no estacionarios. Los resultados experimentales obtenidos muestran una mejoría significativa sobre un método existente.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/715/693
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dc.rightshttps://creativecommons.org/licenses/by/3.0/deed.es_ESen-US
dc.sourceTecnoLógicas; Vol. 20 No. 39 (2017); 185-195en-US
dc.sourceTecnoLógicas; Vol. 20 Núm. 39 (2017); 185-195es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectStackelberg gamesen-US
dc.subjectnon-stationary bi-level optimizationen-US
dc.subjectdifferential evolutionen-US
dc.subjectadaptationen-US
dc.subjectJuegos de Stackelberges-ES
dc.subjectoptimización evolutiva de dos niveles no estacionariaes-ES
dc.subjectevolución diferenciales-ES
dc.subjectauto-adaptaciónes-ES
dc.subjectpruebas no paramétricases-ES
dc.titleOptimization of non-stationary Stackelberg models using a self-adaptive evolutionary algorithmen-US
dc.titleOptimización de modelos de Stackelberg no estacionarios mediante un algoritmo evolutivo auto-adaptativoes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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