The performance improvement of the current Bat Algorithm(BA) lacks rigorous convergence theory, so that the blindness caused by the improvement of the algorithm does not have general theoretical significance.To solve this problem, this paper starts from the perspective of mathematical probability and the state transition of the BA meeting the Markov process.Through the establishment of a reasonable Markov chain model, the transfer behavior of individual status of bats is studied.It proves that the bat population state space is reducible and homogeneous, and the BA satisfies the convergence criterion of stochastic algorithm theoretically.It can converge to the global optimal solution with the probability of 100%.%针对当前蝙蝠算法的性能改进缺少严谨的收敛性证明,导致算法的改进不具备明确的理论意义的问题,从数学概率以及蝙蝠算法状态转移满足Markov过程的角度为出发点,通过建立合理的Markov链模型研究蝙蝠个体状态的转移行为,论证蝙蝠群体状态空间具有可约性和齐次性,从理论上证明蝙蝠算法满足随机算法的收敛准则,保证算法能100%收敛到全局最优解.
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