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Revista de Matemática Teoría y Aplicaciones
versão impressa ISSN 1409-2433
Rev. Mat vol.23 no.1 San José Jan./Jun. 2016
Artículos
SC: a novel fuzzy criterion for solving engineering and constrained optimization problems
SC: un nuevo criterio difuso para resolver problemas de ingeniería y de optimización con restricciones
1Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F., C.P. 09340, México. EMail: cobos@xanum.uam.mx
2Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F., C.P. 09340, México. EMail: gamma@xanum.uam.mx
3Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Av. San Pablo 180, Colonia Reynosa Tamaulipas, México D.F., C.P. 02200, México. E-Mail: rigaeral@correo.azc.uam.mx
4Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F., C.P. 09340, México. EMail:plara@xanum.uam.mx
5Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Av. San Pablo 180, Colonia Reynosa Tamaulipas, México D.F., C.P. 02200, México. E-Mail:mgra@correo.azc.uam.mx
6Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Av. San Pablo 180, Colonia Reynosa Tamaulipas, México D.F., C.P. 02200, México. E-Mail:aspo@correo@correo.azc.uam.mx
In this paper a novel fuzzy convergence system (SC) and its fundamentals are presented. The model was implemented on a monoobjetive PSO algorithm with three phases: 1) Stabilization, 2) generation and breadthfirst search, and 3) generation and depth-first. The system SC-PSO-3P was tested with several benchmark engineering problems and with several CEC2006 problems. The computing experience and comparison with previously reported results is presented. In some cases the results reported in the literature are improved.
Keywords: particle swarm optimization (PSO); optimization
En este trabajo se presenta un novedoso sistema de convergencia (SC), sus fundamentos y la experiencia computacional. Se implementó en un algoritmo PSO monoobjetivo de tres fases: Estabilización, generación y búsqueda en amplitud, generación y búsqueda a profundidad, el cual se probó con diversos problemas benchmark tanto de ingeniería como de la serie CEC2006. La experiencia computacional y la comparación con resultados previamente reportados se presenta. En algunos casos, se mejoran los resultados de la literatura.
Palabras clave: optimización por enjambres de partículas; optimización
Acknowledgements
The authors wish to thank the three anonymous referees since their comments helped to considerably improve the present work.
S.G. de C. would also like to thank D.Sto. and to P.V.Gpe. for their inspiration, and to Ma, Ser, Mon, Chema, and to his Flaquita for all their support
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Received: October 02, 2014; Revised: October 05, 2015; Accepted: October 06, 2015
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