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Identified cluster of atmospheric discharges, sufficiently near from transmissions line, could be an important alarm to support real time decisions. Lightning are important events that affect the electrical power system operation, which are often responsible for transmission lines outages, and can trigger a sequence of events that lead to system collapse. The Brazilian lightning network detection monitors nearly 18 million events monthly and all this data must be processed and analyzed. This paper uses a hybrid model named the Quantum binary-real evolving Spiking Neural Network (QbrSNN) for clustering problem, where the features and parameters of a spiking neural network (SNN) are optimized using the Quantum-Inspired Evolutionary Algorithm with representation Binary-Real (QIEA-BR). The proposed model is applied to atmospheric discharges data, with a significantly higher clustering accuracy than traditional techniques. [ABSTRACT FROM AUTHOR]

The article discusses various papers published within the issue that reflects on hybrid intelligent systems in ensembles. The papers include "Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers," by Ishibuchi and Nojima, "Using weighted dynamic classifier selection methods in ensembles with different levels of diversity," by Canuto and associates and "Constructing Ensembles of Symbolic Classifiers", by Bernardini and associates.