Improved Firefly Algorithm and Its Application

Aiming at the problem that the Firefly algorithm (FA) is prone to fall into local extremum, low precision and slow convergence speed, an improved Firefly algorithm (OLLevyFA) based on reverse learning initialization and Levy perturbation mechanism is proposed on the basis of the Firefly algorithm (FA). Through the calculation of nine typical test functions, the FA algorithm, LevyFA algorithm and OLLevyFA algorithm compared and verified. The results show that compared with the FA algorithm, the LevyFA algorithm and the OLLevyFA algorithm can jump out of the local optimum more effectively, and the OLLevyFA algorithm has the characteristics of higher precision and faster convergence speed. Finally, the FA algorithm, LevyFA algorithm and OLLevyFA algorithm are put into the magnetization temperature measurement model. The comsol software is used to simulate the calculation results and the results of the particle swarm optimization algorithm. The results show that the results of OLLevyFA algorithm have higher precision and can meet the requirements of the system.