This paper proposes a methodology for reconfiguration of Electrical Distribution Systems (EDS) to maximize the system voltage stability. The proposed approach is based on the metaheuristic and... Show More

This paper proposes a methodology for reconfiguration of Electrical Distribution Systems (EDS) to maximize the system voltage stability. The proposed approach is based on the metaheuristic and bioinspired optimization technique called Artificial Immune System (AIS) and stability indexes. The optimization problem presents operational and network constraints, as well as the radial and connected operation and the nodal voltage limits. Comparisons between two indexes are made for evaluate their impact on the reconfiguration problem. Three case studies are presented to assess the proposed approach and the stability indexes.

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is... Show More

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is performed by solving an inverse problem and ill-posed. Evolutionary Computation and Swarm Intelligence have become a source of methods for solving inverse problems. Fish School Search (FSS) is a promising search and optimization method, based on the dynamics of schools of fish. In this article the authors present a method for reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The method was evaluated using numerical phantoms consisting of electrical conductivity images with subjects in the center, between the center and the edge and on the edge of a circular section, with meshes of 415 finite elements. The authors performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.

The template matching is an important technique used in pattern recognition. The goal is to find a given pattern, of a prescribed model, in a frame sequence. In order to evaluate the similarity of... Show More

The template matching is an important technique used in pattern recognition. The goal is to find a given pattern, of a prescribed model, in a frame sequence. In order to evaluate the similarity of two images, the Pearson's Correlation Coefficient (PCC) is used. This coefficient is calculated for each of the image pixels, which entails an operation that is computationally very expensive. In order to improve the processing time, this paper proposes two implementations for template matching: one using Genetic Algorithms (GA) and the other using Particle Swarm Optimization (PSO) considering two different topologies. The results obtained by the proposed methodologies are compared to those obtained by the exhaustive search in each pixel. The comparison indicates that PSO is up to 236x faster than the brute force exhausted search while GA is only 44x faster, for the same image. Also, PSO based methodology is 5x faster than the one based on GA.

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic... Show More

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.