Abstract
This work presents a procedure to automate the design of Si-integrated radio frequency (RF) discrete tuning varactors (RFDTVs). The synthesis method, which is based on evolutionary algorithms, searches for optimum performance RF switched capacitor array circuits that fulfill the design restrictions. The design algorithm uses the c-dominance concept and the maximin sorting scheme to provide a set of different Solutions (circuits) well distributed along an optimal front in the parameter space (circuit size and component values). Since all the solutions present the same performance, the designer call Select the circuit that is best suited to be implemented in a particular integration technology. To assess the performance of the synthesis procedure, several RFDTV circuits, provided by the algorithm, were designed and simulated rising a 0.18 mu m CMOS technology and the Cadence Virtuoso Design Platform. The comparisons between the algorithm and circuit; simulation results show that they are very close, pointing out that; the proposed design procedure is a powerful design tool.

Abstract
This article reports the study of fractional dynamics during the evolution of a Particle Swarm Optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle. The dynamics is represented through the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence of the PSO parameters influence upon the global dynamics is also analyzed.

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Obtaining a well distributed non-dominated Pareto front is one of the key issues in multi-objective optimization algorithms. This paper proposes a new variant for the elitist selection operator to the NSGA-II algorithm, which promotes well distributed non-dominated fronts. The basic idea is to replace the crowding distance method by a maximin technique. The proposed technique is deployed in well known test functions and compared with the crowding distance method used in the NSGA-II algorithm. This comparison is performed in terms of achieved front solutions distribution by using distance performance indices.

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