A new adaptive crow search algorithm was proposed to solve the shortcomings of the original crow search algorithm, such as weak control of population diversity, single updating of individual position and low precision of local search. Firstly, multiple search-guided individuals were designed, and an adaptive selection strategy of search-guided individuals was realized based on population diversity at different stages of evolution. The global exploration in the early iteration and local exploitation in the late iteration were achieved using the strategy. Secondly, by combining the idea of sine-cosine search, several flight length control parameters based on linear decline or mixed sine-cosine oscillation decline were used to constructed different search modes for improving the search ergodicity of the algorithm and increasing the probability that the algorithm finding a better solution in the late iteration. Thirdly, to verify the effectiveness of the new algorithm, standard test functions were selected, and the new algorithm was simulated with the original crow search algorithm, the improved crow search algorithms, and other excellent intelligent optimization algorithms. All the algorithms were compared and analyzed in terms of convergence accuracy, convergence speed, stability, Wilcoxon signed rank and Friedman tests. Experimental results show that the performance of the new algorithm is better than that of other comparison algorithms, and the balance between global exploration and local exploitation, convergence accuracy and convergence speed are achieved by the new algorithm.