An improved cuckoo search (CS) algorithm focusing on optimizing the updating of the bird’s nest position is proposed to overcome the shortcomings of CS algorithm, such as low search accuracy, easy premature convergence, and weak local search ability in the late stage. The proposed algorithm employs three main strategies. First, in the early and middle stage, aiming to expand the search domain, increase the population diversity, and prevent the premature convergence of the algorithm, the global optimization strategy combining opposition-based learning and Levy flight is used to update the nest position. Second, to avoid the algorithm from falling into a local optimum, a dynamic inertia weight is employed to reduce the effect of the current nest position when the nest position is updated during the random migration. Third, in the late stage, to improve the local search ability and search accuracy, the nest position is updated using the local exploitation strategy of the Aquila optimization (AO) algorithm to replace the Levy flight mechanism. The results of comparative experiments with CS algorithm and its four variants on function optimization show that the proposed algorithm has a better global search ability, better local development ability, faster convergence speed and better search accuracy than other selected algorithms.
原文链接
分享
引用
