Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

Abstract

In order to solve the problem that the traditional genetic algorithm has a slow search speed and is easy to fall into the local optimal solution, a mathematical modeling method of an improved genetic algorithm in random power fluctuation is proposed. Drawing on the idea of a genetic algorithm (GA) and using the randomness and stability trend of cloud droplets of the normal cloud model, the author proposes a new genetic algorithm, cloud genetic algorithm (CGA). CGA is implemented by the Y-condition cloud generator of the normal cloud model to realize the cross operation, and the basic cloud generator realizes the mutation operation. Finally, the power function optimization experiment and the IIR digital filter optimization design are carried out, and the standard GA, NQGA, CAGA, and LARES algorithms are carried out compared. The experimental results show that the IIR digital filter designed by CGA has the smallest maximum ripple (Ap) in the passband, which is 0.342, and the largest minimum attenuation (As) in the stopband, which is 34.27. It can be observed that the overall performance is better. The validity of the algorithm is proved, and it has a certain reference and application value.