Cuckoo search algorithm is an emerging bionic intelligent algorithm, which has the shortages of low search precision, easy to fall into local optimum and slow convergence speed. Double cuckoo search algorithm with dynamically adjusted probability (DECS) is proposed. Firstly, the population distribution entropy is introduced into the adaptive discovery probability P, and the size of the discovery probability P is dynamically changed by the iteration order of the algorithm and the population distribution situation. It is advantageous to balance the ability of cuckoo algorithm local optimization and global optimization and accelerate the convergence speed. Secondly, in the formula for updating the path position of cuckoo??s nest search, a new step-size factor update and optimization method is adopted to form a double search mode of Levy flight, which sufficiently searches the solution space. Finally, the nonlinear logarithmic decreasing inertial weight is introduced into the updated formula of stochastic preference walk, so that the algorithm can effectively overcome the shortcoming of being easily trapped into a local optimum, and improve search ability. Compared with four algorithms, simulation results of 19 test functions show that, the optimization performance of the improved cuckoo algorithm is significantly improved, the convergence speed is faster, the solution accuracy is higher, and it has stronger ability of global search and jumping out of local optimum.

%U http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2004031