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In evolutionary algorithms, it is difficult to balance the exploration and exploitation. Usually, global search is utilized to find promising solutions, and local search is beneficial to the convergence of the solutions in the population. Combing different search strategies is a promising way to take advantages of different methods. Following the idea of DE/EDA, this paper proposes another way to...
It is well known that in evolutionary algorithms (EAs), different reproduction operators may be suitable for different problems or in different running stages. To improve the algorithm performance, the ensemble of multiple operators has become popular. Most ensemble techniques achieve this goal by choosing an operator according to a probability learned from the previous experience. In contrast to...
This paper presents a novel high-resolution signal processing technique for non-intrusive detection of broken bar fault condition in induction machine rotor. The technique is based on parametric spectral estimation of stator current waveform recorded while a machine is running. The frequency components that are related to broken rotor bar condition are very close to the fundamental frequency, and...
PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation...
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