@Article{SoterroniGalsScarRamo:2015:QvStDe,
               author = "Soterroni, Aline Cristina and Galski, Roberto Luiz and Scarabello, 
                         Marluce da Cruz and Ramos, Fernando Manuel",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "The q-G method : a q-version of the steepest descent method for 
                         global optimization",
              journal = "Springer Plus",
                 year = "2015",
               volume = "4",
               number = "1",
                pages = "647",
                month = "Dec.",
             keywords = "Jackson’s derivative, q-Derivative, q-G method, q-Gradient.",
             abstract = "In this work, the q-Gradient (q-G) method, a q-version of the 
                         Steepest Descent method, is presented. The main idea behind the 
                         q-G method is the use of the negative of the q-gradient vector of 
                         the objective function as the search direction. The q-gradient 
                         vector, or simply the q-gradient, is a generalization of the 
                         classical gradient vector based on the concept of Jacksons 
                         derivative from the q-calculus. Its use provides the algorithm an 
                         effective mechanism for escaping from local minima. The q-G method 
                         reduces to the Steepest Descent method when the parameter q tends 
                         to 1. The algorithm has three free parameters and it is 
                         implemented so that the search process gradually shifts from 
                         global exploration in the beginning to local exploitation in the 
                         end. We evaluated the q-G method on 34 test functions, and 
                         compared its performance with 34 optimization algorithms, 
                         including derivative-free algorithms and the Steepest Descent 
                         method. Our results show that the q-G method is competitive and 
                         has a great potential for solving multimodal optimization 
                         problems.",
                  doi = "10.1186/s40064-015-1434-4",
                  url = "http://dx.doi.org/10.1186/s40064-015-1434-4",
                 issn = "2193-1801",
             language = "en",
           targetfile = "2015_soterroni.pdf",
        urlaccessdate = "03 jun. 2024"
}