Abstract
Particle Swarm Optimization (PSO) is a population-based search algorithm that is initialized with a population of random solutions, called particles. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. PSO is also an evolutionary computation technique well adapted to the automatic design of electronic devices. In this line of thought, this paper proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. The results are compared with other two Evolutionary Algorithms (EAs), namely Genetic and Memetic Algorithms (GA and MA).

Abstract
The concept of differentiation and integration to non-integer order has its origins in the nineteen century. However, only in the second-half of the twenty century appeared the first applications related to the area of control theory. In this paper we consider the study of a heat diffusion system based on the application of the fractional calculus concepts. In this perspective, several control methodologies are investigated and compared, such as the adoption of a Smith predictor structure for the better control of systems with time delays. Several simulations are presented assessing the performance of the proposed fractional-order algorithms.

Abstract
This paper presents several experiments with a genetic algorithm (GA) for designing combinational logic circuits. The study addresses the population size and the processing time for achieving a solution in order to establish a compromise between the two parameters. Furthermore, it is also investigated the use of different gate sets for designing the circuits namely RISC and CISC like gate sets.

Abstract
Fuzzy modelling has been widely applied as a powerful methodology for the identification of nonlinear systems from process measurements. Most applications use flat sets of fuzzy rules, which are hardly interpretable black-box approaches. Hierarchical modelling is a promising tool to deal with real world complex systems. A large-scale model can be easily readable if it is partitioned into several independent smaller models to represent functional relations of the processes involved in the system. This article deals with the application of a new fuzzy modelling technique that automatically organizes the sets of fuzzy IF-THEN rules in a Hierarchical Collaborative Structure. This organizational structure makes the fuzzy model interpretable as in the case of the physical model. This new methodology was tested to split the inside greenhouse air temperature and humidity flat fuzzy models into fuzzy sub-models. which have alike counterpart on the physical sub-models.

Abstract
The internal impedance of a wire is the function of the frequency. In a conductor, where the conductivity is sufficiently high, the displacement current density can be neglected. In this case, the conduction current density is given by the product of the electric field and the conductance. One of the aspects of the high-frequency effects is the skin effect (SE). The fundamental problem with SE is it attenuates the higher frequency components of a signal. The SE was first verified by Kelvin in 1887. Since then many researchers developed work on the subject and presently a comprehensive physical model, based on the Maxwell equations, is well established. The Maxwell formalism plays a fundamental role in the electromagnetic theory. These equations lead to the derivation of mathematical descriptions useful in many applications in physics and engineering. Maxwell is generally regarded as the 19th century scientist who had the greatest influence on 20th century physics, making contributions to the fundamental models of nature. The Maxwell equations involve only the integer-order calculus and, therefore, it is natural that the resulting classical models adopted in electrical engineering reflect this perspective. Recently, a closer look of some phenomenas present in electrical systems and the motivation towards the development of precise models, seem to point out the requirement for a fractional calculus approach. Bearing these ideas in mind, in this study we address the SE and we re-evaluate the results demonstrating its fractional-order nature.

Abstract
This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity Evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical. evaluation. The dynamic-fitness function results from an analogy with control systems where it is possible to benefit the proportional algorithm by including a differential scheme. It is investigated the CA performance when adopting each concept separately. The experiments reveal superior results, in terms of speed and convergence of the number of iterations required to achieve a, solution. In a final phase the two concepts are integrated in the GA fitness function leading to the best performance.