Abstract
Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method.
Highlights
Many practical signals, such as voice and audio signals, power system transient signals, and response signals of sensors, are recognized as signals of the sum of sinusoidal signals
In the light of these facts, authors are motivated in this work to design an enhanced genetic algorithm (EGA), through a prejudice-free selection mechanism for preserving population diversity, diversified individuals with a two-step crossover (TSC) operator, and a good cooperation of fast convergence and searching performance with an adaptive mutation strategy, for estimating the parameters of sinusoidal signals
Considering the same dataset, it was reported that the fitted frequency by the elemental set method [48] was 0.0873, which once again is close to our frequency estimation
Summary
Many practical signals, such as voice and audio signals, power system transient signals, and response signals of sensors, are recognized as signals of the sum of sinusoidal signals. In the light of these facts, authors are motivated in this work to design an enhanced genetic algorithm (EGA), through a prejudice-free selection mechanism for preserving population diversity, diversified individuals with a two-step crossover (TSC) operator, and a good cooperation of fast convergence and searching performance with an adaptive mutation strategy, for estimating the parameters of sinusoidal signals. Validation through the results of a comparative analysis in terms of performance-monitoring metrics based on the mean, coefficients of determination (R2 ), and the sum of squared residuals value (SSR) of each algorithm; An intelligible concept with easy realization and a worry-free tradeoff between global and local search in comparison to general hybrid techniques.
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