Nuevos artículos publicados


Hemos publicado dos nuevos artículos. El primero de ellos en el área de algoritmos evolutivos:

J.M. Herrero, G. Reynoso-Meza, M. Martínez, X. Blasco, J.Sanchis. A Smart-Distributed Pareto Front Using the ev-MOGA Evolutionary Algorithm. International Journal on Artificial Intelligence Tools, Volume 23, Issue 02, April 2014.



Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

J.M. Herrero et Al. 2014

El segundo de ellos en el área de ingeniería de control:

G. Reynoso-Meza, X. Blasco, J. Sanchis, M. Martínez. Controller tuning using evolutionary multi-objective optimisation: Current trends and applications. Control Engineering Practice Volume 28, June 2014, Pages 58–73


Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.

G. Reynoso-Meza et Al. 2014

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