A Novel Genetic Algorithm Applied to Parameter Estimation of Passive Surface Acoustic Wave Torque Sensing Signal

초록

This paper presents a novel genetic algorithm (GA) for the parameter estimation of surface acoustic wave (SAW) sensor signal. Differently from conventional GA, a two-step crossover operation is proposed for better interchange between variables and individuals. Besides, to resolve the problem of premature convergence and to increase the population diversity, an adaptive mutation mechanism is integrated as well. In order to verify the performance of the proposed method, benchmark function based studies using traditional GA, particle swarm optimization (PSO), and our proposed algorithm are implemented. At the end, parameter estimations for simulated signal are carried out. All the results highlight the superiority of our algorithm.

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