针对标准布谷鸟搜索(CS)算法存在全局搜索和局部搜索能力不平衡的缺点, 提出一种基于梯度的自适应快速布谷鸟搜索(GBAQCS)算法. 在改进的算法中, 针对偏好随机游动的步长, 在利用目标函数的梯度决定步长方向的基础上, 首先提出自适应搜索机制平衡了算法的全局搜索和局部搜索能力; 其次提出快速 搜索策略, 充分利用当前鸟巢信息进行精细化搜索, 从而提高算法的搜索精度和收敛速度. 实验结果表明, 相比其他算法, 所提出的改进策略使算法的全局搜索和局部搜索能力保持了相对的平衡, 并提高了算法的收敛性能.

 For the problems of the unbalanced capability between global search and local search of standard cuckoo search (CS) algorithm, a gradient-based adaptive quick cuckoo search (GBAQCS) is proposed. The direction of the step is determined based on the sign of the gradient of the function. On the one hand, the adaptive search strategy is used to balance the global search and local search capability. On the other hand, the current-guided search method is adopted to improve the convergence precision and rate. The simulation experiments show that GBAQCS fully utilizes and balances the global search and local search capability, and greatly improves the convergence speed and quality of solutions compared with other optimization algorithms.