目的 为了解决在求解复杂的高维函数优化问题时存在的求解精度不够高和易陷入局部最优等问题，提出一种基于莱维飞行发现概率的变步长布谷鸟搜索算法(LFCS)。方法 在相同环境下，选取6个不同难度、不同类型的测试函数，将LFCS算法与IPSO，IDE，IABC，CS算法比较，分析算法的收敛速度和收敛精度。结果 相比其他4种算法，LFCS算法迭代次数更少，收敛速度更快，收敛精度更高。结论 无论是低维函数还是高维函数，LFCS算法在收敛速度和收敛精度方面都有所提高，尤其是针对复杂的高维函数优化问题，在取值范围较大的情况下，LFCS算法能够更快、更准地找到最优解。

The work aims to propose a variable-step cuckoo search algorithm (denoted as LFCS) based on Levi's flight detection probability to solve the problems in solving complex high-dimensional function optimization, such as insufficient accuracy and easy to fall into local optimization. Under the same environment, six test functions of different difficulty and different types were selected, and the LFCS algorithm was compared with the IPSO, IDE, IABC, and CS algorithms to analyze the convergence speed and convergence accuracy of the algorithm. Compared with the other four algorithms, the LFCS algorithm had fewer iterations, faster convergence speed, and higher convergence accuracy. No matter whether it is a low-dimensional function or a high-dimensional function, the LFCS algorithm has improved convergence speed and convergence accuracy. Especially for complex high-dimensional function optimization problems, the LFCS algorithm can find the optimal solution faster and more accurately when the value range is large.