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Given a non-optimal Yagi-Uda antenna, this paper presents a technique to improve simultaneously and monotonically all its conflicting specifications. This is possible using the multiobjective optimization technique called Dominating Cone Line Search. Results for a 6 elements antenna, considering 3 conflicting specifications in 3 different frequencies, thus, a 9 objectives optimization problem, is presented. Index Terms--Multi-objective optimization and design, Yagi-Uda antenna.

A meshless method for electromagnetic field computation is developed based on the multiquadric interpolation technique. A global approximation to the solution is built based only on the discretization of the domain in nodes and the differential equations describing the problem in the domain and its boundary. An attractive characteristic of the multiquadric solution is that it is continuous and it has infinitely continuous derivatives. This is particularly important to obtain field quantities in electromagnetic analysis. The method is also capable of dealing with physical discontinuities present at the interface between different materials. The formulation is presented in the Cartesian and polar coordinates, which can be extended to other systems. We applied the formulation in the analysis of an electrostatic micromotor and a microstrip. The results demonstrate good agreement with other numerical technique, showing the adequacy of the proposed methodology for electromagnetic analysis. Index Terms--Collocation methods, interface conditions, mesh-free methods, meshless methods, multiquadrics.

This paper presents a multiobjective approach for the design of electrical distribution networks. The objectives are defined as a monetary cost index (including installation cost and energy losses cost) and a system failure index. The true Pareto-optimal solutions are found with a multiobjective genetic algorithm that employs an efficient variable encoding scheme and some problem-specific mutation and crossover operators. Results based on 21- and 100-bus systems are presented. The information gained from the Pareto-optimal solution set is shown to be useful for the decision-making stage of distribution network evolution planning. Index Terms--Decision-making, energy distribution networks, genetic algorithms (GAs), multiobjective optimization, network topology optimization.

The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained mono-objective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective is related to the quench constraint and the better use of the superconducting material. The formulation proposed yields results that provide new alternatives to the designer. We solved the formulation proposed using the multiobjective clonal selection algorithm. After that, we selected a particular solution using a simple decision making procedure. Index Terms--Electromagnetic design, multiobjective optimization, TEAM benchmark problem 22.

Typical formulations for fuzzy optimization utilize piecewise linear membership functions, which introduces regions of no differentiability and prevents the employment of deterministic methods. We propose the utilization of continuously differentiable membership functions, which permit the use of gradient-based methods. We present a general expression for the gradient of the decision degree function. Then, a stochastic method can be used to find a good starting point for the deterministic technique, in a hybrid approach. The formulation is applied to the optimization of an electrostatic micromotor. The average torque is maximized subject to a fuzzy constraint for the torque ripple. The results show the validity of the methodology in electromagnetic design. Index Terms--Electromagnetic design, fuzzy optimization.

This paper presents Optimise, a computational optimization environment tool for education in electrical engineering. Optimise has been developed using software engineering process and object-oriented programming philosophy. This educational tool incorporates a set of deterministic and stochastic methods and also a set of computational intelligence techniques. Optimise offers a friendly interface that allows the students to practice the theory learned, and also to verify and compare the features of the optimization methods. A general view of the software is presented, describing its modules and class libraries. Some optimization problems are discussed to illustrate the flexibility and power of Optimise as an educational tool. Index Terms--Electromagnetics, object-oriented programming, optimization and design.

This brief investigates the use of prior knowledge in the parameter estimation of NARMAX polynomial models. The problem of parameter estimation is then formulated in such a way that the estimated models have specified features. This formulation results in a constrained optimization problem, which is solved using the ellipsoid algorithm. This technique is applied to a real dc-dc Buck converter. In this system, the static relation is known from the theory but identification data are located over a rather narrow range around an operating point. Although obtained from dynamical data, the models provide good approximation to the nonlinear static function. Index Terms--Grey-box identification, NARMAX models, parameter estimation, prior knowledge.

This paper presents a computational tool intended to achieves optimal configurations of conductors with capacity of transmission of energy improved. This arrangement is obtained by calculate and minimize of electric fields at ground level of High Surge Impedance Loading transmission lines. This type of transmission line has higher power transmission rates by utilizing optimized configuration for the conductors. In the present work, an enhanced Deep Cut Ellipsoidal Method is applied to find a new optimized configuration for the phase conductors. The Surge Impedance Loading of the suggested configuration is analyzed. The new proposed configuration results in reduced profiles of electric fields and higher power transmission capacity. [ABSTRACT FROM AUTHOR]

Presents a multiobjective genetic algorithm with a real-biased crossover operator. Application of the proposed algorithm to the optimization of a Yagi-Uda antenna; Definition of a multiobjective optimization problem; Electrical characteristics of the antenna.

Presents a cone ellipsoidal algorithm for nonlinear optimization suitable for dealing with equality constraints. Features of an ellipsoidal method; Details on constraint processing in the classical ellipsoidal method.

This paper presents a new scheme for training MLPs which employs a relaxation method for multiobjective optimization. The algorithm works by obtaining a reduced set of solutions, from which the one with the best generalization is selected. This approach allows balancing between the training error and norm of network weight vectors, which are the two objective functions of the multi-objective optimization problem. The method is applied to classification and regression problems and compared with Weight Decay (WD), Support Vector Machines (SVMs) and standard Backpropagation (BP). It is shown that the systematic procedure for training proposed results on good generalization neural models, and outperforms traditional methods. [ABSTRACT FROM AUTHOR]

This paper proposes a revaluation of the Brazilian wind energy policy framework and the energy auction requirements. The proposed model deals with the four major issues associated with the wind policy framework that are: long-term wind speed sampling, wind speed forecasting reliability, energy commercialization, and the wind farm profitability. Brazilian wind policy, cross-checked against other countries policies, showed to be too restrictive and outdated. This paper proposes its renewal, through the adoption of international standards by Brazilian policy-makers, reducing the wind time sampling necessary to implement wind farms. To support such a policy change, a new wind forecasting method is designed. The method is based on fuzzy time series shaped with a statistical significance approach. It can be used to forecast wind behavior, by drawing the most-likely wind energy generation intervals given a confidence degree. The proposed method is useful to evaluate a wind farm profitability and design the biding strategy in auctions. [ABSTRACT FROM AUTHOR]