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@Article{EchevarriaSantSilvCamp:2014:NeVaDi,
               author = "Echevarria, Lidice Camps and Santiago, Orestes Llanes and Silva 
                         Neto, Ant{\^o}nio Jos{\'e} da and Campos Velho, Haroldo Fraga 
                         de",
          affiliation = "{Instituto Superior Polit{\'e}cnico Jos{\'e} Antonio 
                         Echeverr{\'{\i}}a} and {Instituto Superior Polit{\'e}cnico 
                         Jos{\'e} Antonio Echeverr{\'{\i}}a} and {State University of 
                         Rio de Janeiro (UERJ)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "An Approach of Fault Diagnosis Using Meta-Heuristics: a New 
                         Variant of the Differential Evolution Algorithm",
              journal = "Computacion y Sistemas",
                 year = "2014",
               volume = "18",
               number = "1",
                pages = "5--17",
             keywords = "differential evolution, meta-heuristics, fault diagnosis, particle 
                         collision, robustness, sensitivity.",
             abstract = "This paper presents an application of meta-heuristics to fault 
                         diagnosis. The idea behind this application is to develop methods 
                         for fault diagnosis that should be robust, sensitive and with an 
                         adequate computational cost. Applications of meta-heuristics are 
                         possible based on the formulation of fault diagnosis as an 
                         optimization problem. The results indicate the suitability of the 
                         use of meta-heuristics for fault diagnosis. In particular, this 
                         study shows an application of meta-heuristic termed Differential 
                         Evolution to diagnosing a DC Motor benchmark. This allowed 
                         developing a new variant of Differential Evolution, namely, 
                         Differential Evolution with Particle Collision. This new algorithm 
                         was validated with some benchmark functions for continuous 
                         optimization, showing that it over-performed the behavior of 
                         Differential Evolution.",
                 issn = "1405-5546",
                label = "lattes: 5142426481528206 4 EchevarriaSantSilvCamp:2014:NeVaDi",
             language = "en",
           targetfile = "v18n1a2.pdf",
                  url = "http://cys.cic.ipn.mx/ojs/index.php/CyS/article/viewFile/1578/1881",
        urlaccessdate = "28 nov. 2020"
}


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