Soft Computing Models in Industrial and Environmental by Cristian Pinzón, Angélica González, Manuel Rubio, Javier

By Cristian Pinzón, Angélica González, Manuel Rubio, Javier Bajo (auth.), Emilio Corchado, Paulo Novais, Cesar Analide, Javier Sedano (eds.)

This quantity of Advances in clever and gentle Computing includes authorized papers provided at SOCO 2010 held within the appealing and ancient urban of Guimarães, Portugal, June 2010. This quantity provides the papers authorized for the 2010 variation, either for the most occasion and the unique classes. SOCO 2010 certain periods are a really great tool with a view to supplement the average application with new or rising themes of specific curiosity to the engaging group. distinctive periods that emphasize on multi-disciplinary and transversal facets, in addition to state of the art subject matters have been in particular inspired and welcome.

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Moreover, in this work, we incorporate the hypervolume indicator, allowing future comparisons of other authors. As we will see, our algorithm achieves very good solutions, surpassing other proposals. 1 Introduction The application of evolutionary algorithms for solving optimization problems has been intense in recent decades. An evolutionary algorithm uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. The solutions of an optimization problem play the role of individuals in a population, and a fitness function determines the quality of them.

1, pp. 773–776 (2004) 13. : Electric Power Systems Research 72(2), 147 (2004) 14. : A Time-domain Threephase Power System Impedance Modeling Approach for Harmonic Filter Analysis. IEEE Transactions on Power Delivery 8(2), 504 (1993) A Multiobjective Variable Neighborhood Search for Solving the Motif Discovery Problem ´ David L. Gonz´alez-Alvarez, Miguel A. Vega-Rodr´ıguez, Juan A. G´omez-Pulido, and Juan M. S´anchez-P´erez Abstract. In this work we approach the Motif Discovery Problem (MDP) by using a trajectory–based heuristic.

The current RMS value can be defined by IRMS = ∞ ∑ Hn2 , n=1 which can be rewritten as IRMS = (a) I12 + I32 + I52 + · · · + In2 , (2) (b) Fig. 1 Current and voltage typical consumption waveforms for a PC (a), and its measured harmonic current spectrum (b) 34 J. Baptista et al. where In represents the current associated to the n-th odd harmonic. The greater the n the lower its influence in the IRMS value. Usually the first harmonics will suffice to have a good approximation to the correct value.

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