Ricardo Adriano

Treating constraints as objectives in multiobjective optimization problems using niched Pareto genetic algorithm

In this paper, the constraints, in multiobjective optimization problems, are treated as objectives. The constraints are transformed in two new objectives: one is based on a penalty function and the other is made equal to the number of violated constraints. To ensure the convergence to a feasible Pareto optimal front, the constrained individuals are eliminated during the elitist process. The treatment of infeasible individuals required some relevant modifications in the standard Parks and Miller elitist technique. Analytical and electromagnetic problems are presented and the results suggest the effectiveness of the proposed approach.

A Modified Plane Wave Enrichment to Solve 2-D Electromagnetic Problems Using the Generalized Finite-Element Method

This paper addresses the generalized finite-element method enriched by plane waves for the 2-D Helmholtz equation. The enrichment function is modified to reduce the effect of numerical dispersion from coarse meshes and to make it possible to treat essential boundary conditions in a straightforward way. Two numerical examples, the free space propagation problem and the plane wave scattering by a cylinder, demonstrate the effectiveness of the new enrichment function.

Improving helical antennas using a deep-cut ellipsoidal algorithm

In this paper, uniform helical antennas are used as starting point to generate optimized non-uniform ones. The improvements in the input impedance and gain are obtained varying the shape of the helix using a second order polynomial function. The optimal polynomial parameters are found using the deep-cut version of the ellipsoidal optimization algorithm. The use of the well known formulae and diagrams for the design of uniform helical antennas as a initial point of the deterministic method can reduce the search domain and provides a fast convergence rate. Results are presented for WiFi application using a center frequency of 2.45 GHz. It shows that the input impedance and the antenna length can be considerably improved using a simple and efficient parametrization.

Improvement of System Quality in a Generalized Finite-Element Method Using the Discrete Curvelet Transform

The generalized finite-element method (GFEM) enriched by plane waves has been proved suitable to solve the 2-D Helmholtz equation. However, when the number of unknowns increases, some difficulties arise, such as bad condition number. Therefore, developing a pre-processing methodology to identify an appropriate number of degrees of freedom for each problem could be useful. This paper presents a systematic approach to determine the optimal number of plane waves for each problem to be used in GFEM solution. It is based on the analysis of the traditional FEM solution with a poor mesh resolution using the fast discrete curvelet transform. The results indicate that the adopted strategy can guarantee accurate and converging responses for GFEM in complex problems, along with remarkable reductions of the computational cost.

Multiobjective design optimization of non-uniform helical antennas

Multiobjective optimization evolutionary algorithm was used to find a set of optimum designs for non-uniform helical antennas, with turns varying from 3 to 9. The antenna parametrization proposed herein allows radii continuous variation with independent parameter per turn. The optimization procedure of antennas designs aimed to: a) maximize axial gain; b) minimize length; c) minimize impedance matching deviation. Optimum designs were generated using a center frequency of 2.45 GHz for WiFi applications. A representative design was selected for each turn, whose results were, thereafter, compared with benchmarking results for non-uniform helical antenna. The comparison of the results indicates a general improvement on antennas characteristics, mainly for antennas with number of turns higher than 6.