基于高斯扰动和指数递减策略的改进蝙蝠算法 后印本

Краткое изложение: Aiming at the shortcomings of the basic bat algorithm such as slow convergence, low convergence precision and weak stability, this paper designed an improved bat algorithm (GDEDBA) based on Gaussian disturbance and exponential decreasing strategy. It introduced the exponential decreasing strategy into the speed update formula, could enable the algorithm to enter local optimization quickly and exactly; it added the constructed Gaussian disturbance term to the local new solution generation formula, then the information exchange and study between all particles in the local new solution and the current global optimal particles cloud happened, prevented falling into local optimum and increased population diversity; it designed the disturbance control factor to control the disturbance range of Gaussian disturbance, enhanced the stability of the algorithm. The simulation results of 15 classical test functions shows that the optimization performance of improved algorithm is significantly improved, the convergence speed is faster, the solution accuracy is higher, and the stability is stronger.