Abstract-Due to an increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have developed a number of real-parameter genetic algorithms (GAs) in the recent past. In such studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create offspring. Some operators emphasize solutions at the center of mass of parents and some emphasize solutions around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-centric recombination operators (UNDX and SPX). With the help of a steady-state, elite-preserving, and computationally fast EA model, the simulation results show the superiority of PCX over mean-centric operators on three test problems.
In this paper, genetic algorithm-based technique is proposed to estimate and analyze the steady-state performance of a self-excited induction generator (SEIG). The study reveals that the performance of the SEIG is greatly influenced by the operating speed, load, and excitation capacitance. This provides an opportunity for the proper handling of these parameters so as to obtain the required performance characteristics. In this paper, a new technique is proposed to identify these parameters to achieve constant voltage constant frequency operation for SEIG. Theoretical results as obtained have been compared with experimental results and found to be in close agreement.Index Terms-Genetic algorithm (GA), self-excited induction generator (SEIG), wind energy generation.
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