基于高斯扰动和指数递减策略的改进蝙蝠算法

针对基本蝙蝠算法后期收敛速度慢、收敛精度不高、稳定性不强等问题，提出基于高斯扰动和指数递减策略的改进蝙蝠算法（GDEDBA）。将指数递减策略引入速度更新公式，使算法迅速进入局部寻优并展开精确搜索；构造高斯扰动项加入到局部新解产生公式，使局部新解中所有粒子与当前全局最优粒子产生信息交流与学习，防止陷入局部最优，增加种群多样性；设计扰动控制因子来控制高斯扰动的扰动范围，增强算法的稳定性。15个测试函数的仿真结果表明，改进算法的寻优性能显著提高，收敛速度更快，求解精度更高，稳定性更强。

Improved bat algorithm based on Gaussian disturbance and exponential decreasing strategy

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 based on Gaussian disturbance and exponential decreasing strategy(GDEDBA). 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 gene-ration formula, then made the information exchange and study between all particles in the local new solution and the current global optimal particles, 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 show 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.