Abstract: Aiming at the problems of slow convergence speed and local optimization of crow algorithmthis paper proposes a cauchy mutation crow algorithm with adaptive step size (CMCSA)to improve the position updating strategy of two situations in standard crow algorithm.In each iterationthe Cauchy mutation is used to optimize the gbestto enhance the global searchcapabi-lity and increase the variation rangeso as to improve the population diversity and avoid falling into local optimization.The discriminant probability is introduced to optimize the updating strategy of the current individual's position when the leader finds himself followed.The step length is adjusted adaptively according to the position distance between the current position and the leader's positionso that the algorithm converges smoothly and quickly to the global optimumthus controlling the search speed and accuracyand effectively compensating for the blindness and slow convergence of the standard CSA.In order to evaluate the effectiveness of the algorithmthe proposed CMCSA is applied to optimize ten basic test functionsand compared with eight other famous and recent intelligent optimization algorithms.The experimental results show that the proposed algorithm is superior to other algorithms in average convergence and robustness.The average ranking of the mean value and standard deviation value of the algorithm is the firstso it has better overall performance.

Key words: Adaptive step-size, Cauchy mutation, Crow algorithm, Function optimization