引入PSO加速因子和个体认知的改进蝙蝠算法

针对蝙蝠算法后期收敛速度慢，易陷入早熟收敛，求解精度低的缺点，提出一种引入粒子群算法中的个体认知与加速因子的改进方法。该方法增加了蝙蝠的个体历史飞行经验，提高了个体自主性，以避免群体经验过度影响带来的进化能力丧失；利用加速因子对速度的控制，增加蝙蝠的可飞行范围，提高搜索能力，加快收敛速度。最后选取标准测试函数对设置不同加速因子的改进算法进行仿真验证，并与基本蝙蝠算法进行对比，结果显示改进后的算法在收敛速度和求解精度上有进一步提高。

Improved bat algorithm with PSO acceleration factor and individual cognition

In view of the basic bat algorithm has a few problems in slow convergence rate,high possibility of being trapped in premature convergence and low optimization accuracy,a new improving method of introducing the individual cognition and acceleration factor of the particle swarm optimization(PSO)algorithm is proposed.This method increases the individual flying experience to improves the individual autonomy and avoid the loss of evolutionary power brought about by excessive influence of group experience.Using acceleration factor to control bat flight speed to increase the flying range to improve the search ability and speed up the convergence.Finally,the improved algorithm with different mode of acceleration factor is verified by the standard test function,and compared with the basic bat algorithm.The results show that the convergence speed and solution precision of improved algorithm is are further enhanced.