Adriano C. Lisboa

A Multi-Objective Evolutionary Algorithm Based on Decomposition for Optimal Design of Yagi-Uda Antennas

This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to design broadband optimal Yagi-Uda antennas. A multi-objective problem is formulated to achieve maximum directivity, minimum voltage standing wave ratio and maximum front-to-back ratio. The algorithm was applied to the design of optimal 3 to 10 elements Yagi-Uda antennas, whose optimal Pareto fronts are provided in a single picture. The multi-objective problem is decomposed by Chebyshev decomposition, and it is solved by differential evolution (DE) and Gaussian mutation operators in order to provide a better approximation of the Pareto front. The results show that the implemented MOEA/D is efficient for designing Yagi-Uda antennas.

A Multiobjective Heuristic for Reconfiguration of the Electrical Radial Network

This paper proposes a new reconfiguration heuristic in order to reduce the total power loss and the maximum current of electrical radial networks. It is based on the branch-and-bound strategy, which is an implicit enumeration method that uses a tree structure and bounds to organize the searching process. The search tree in this paper is constructed by subdividing the feasible set using the branch-exchange technique in the networks. The constraints and the Pareto dominance are responsible for pruning the search tree. The heuristic also returns a feasible switching plan for each solution. The algorithm was successfully applied to medium- and large-scale problems.

Multiobjective shape optimization of broad-band reflector antennas using the cone of efficient directions algorithm

This paper presents a multiobjective shape optimization of an offset reflector antenna using the Cone of Efficient Directions Algorithm (CEDA), that includes a multiobjective line search. This algorithm features a monotone dominance convergence: the sequence of solution antennas occur in such a way that all the objectives are improved simultaneously. Given an area to be covered, the desired radiation pattern is the one with the maximum mean gain and uniformity, possibly weighted, inside it. These features are achieved by the definition of a single objective function, which must held in three different frequencies (a total of three conflicting objectives), in order to provide broad-band characteristics to the designed antenna

An Enhanced Ellipsoid Method for Electromagnetic Devices Optimization and Design

This paper proposes a novel ellipsoid method for the optimization of electromagnetic constrained problems. Unlike the classical method, which can apply only one cut per iteration, this novel algorithm can employ multiple cuts simultaneously. This improves the convergence rate while preserving all theoretical guarantees of the original method. The design of modelled reflector antennas for optimal coverage of the Brazilian, Chinese, and American territories is presented. These problems have 38 control variables and numerous constraints (18, 11, and 12). Several results are presented for the enhanced and classical ellipsoid methods, as well as for stochastic algorithms. They assert the efficiency of the introduced technique.

Efficient algorithms and data structures for element-free Galerkin method

The element-free Galerkin method (EFG) has specific characteristics that require the usage of techniques and data structures in order to provide efficient calculation. This paper address two problems concerning the EFG implementation. The point location problem, which must find in which subdomain the integration point is located, and the influence domain problem, which must find the nearest nodes to build an influence domain and construct the shape functions. This work proposes the use of new data structures and algorithms in order to solve these problems, speeding up the method and providing a fast and correct influence domain construction

Line search methods with guaranteed asymptotical convergence to an improving local optimum of multimodal functions

This paper considers line search optimization methods using a mathematical framework based on the simple concept of a v-pattern and its properties. This framework provides theoretical guarantees on preserving, in the localizing interval, a local optimum no worse than the starting point. Notably, the framework can be applied to arbitrary unidimensional functions, including multimodal and infinitely valued ones. Enhanced versions of the golden section, bisection and Brent’s methods are proposed and analyzed within this framework: they inherit the improving local optimality guarantee. Under mild assumptions the enhanced algorithms are proved to converge to a point in the solution set in a finite number of steps or that all their accumulation points belong to the solution set.

A Closed-Form Solution for Untransposed Transmission-Lines Fault Location With Nonsynchronized Terminals

This paper introduces a novel method for fault location in alternate current transmission lines relying on the voltage and current measurements in both line ends. The novelty of this method lies in the fact that there is no need for terminal synchronization and it can be applied to general untransposed transmission lines. The proposed formulation only applies a closed-form solution; thus, no iterative algorithm is required. Several simulated and real-world results are presented, and they show the effectiveness of the proposed approach.

Monotonically Improving Yagi-Uda Conflicting Specifications Using the Dominating Cone Line Search Method

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.

A Closed-Form Solution for Transmission-Line Fault Location Without the Need of Terminal Synchronization or Line Parameters

This letter introduces a novel method for fault location in alternate current transmission lines where neither the line parameters nor terminals synchronization is needed. As opposed to the previous works, it introduces a closed-form solution instead of iterative methods. The results are presented for simulated and real-world problems, indicating that the proposed algorithm attains good performance in the tested cases.

A Recursive Sparsification of the Inverse Hodge Matrix

When applying the theory of differential forms to solve wave propagation problems in time domain, we must solve at each time step a sparse linear system defined by the insertion of constitutive laws via the mass matrices. In this paper, we describe a recursive technique to efficiently calculate the approximated inverse of Hodge matrix. The fundamental idea is to recursively decompose the mass matrix in to a decreasing size sequence of matrices using block matrix inversion. During the recomposition process, the matrix is sparsified. Numerical results are presented to validate our approach.