Abstract: Cuckoo search algorithm，inspired by the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flights behavior of some birds and fruit flies，iteratively uses Lévy flights random walk to search for new solutions.During Lévy flights random walk，the new solutions are based on the current solutions，the best one obtained so far，and a fixed and small scaling factor.This factor is employed to ensure Lévy flights random walk not to be too aggressive to avoid jumping out of search space.In this case，the small and fixed factor is a favor of the exploitation ability when the current solutions are nearby the best one，but goes against the exploration ability in the case that the current solutions are far away from the best one.This paper is along with the varied factor strategy，and proposes an enhanced cuckoo search algorithm with beta distribution.In proposed algorithm，the beta sequence factors obeyed the beta distribution are used to take place the small and fixed one so that the different small and large factors are dynamical during the search.Therefore，the exploitation and exploration abilities are enhanced，resulting in the better solution quality and the quicker convergence speed.The comprehensive simulation experiments，which were carried on a suit of 20 benchmark functions，show that the proposed algorithm is available and effective，and the achieved solutions accuracy and convergence speed using beta random sequence factor is overall better than those obtained by using the fixed factor and uniform random sequence factor.The results also show that the accuracy of solutions obtained by the enhanced algorithm is stable when the dimension of problem increases.The discussion reveals that the beta random sequence factor can be integrated into other improved algorithms easily and effectively.