基于骑手优化的动态布谷鸟搜索算法

针对传统布谷鸟搜索算法（cuckoo search，CS）对复杂问题收敛精度低、迭代步幅局限性大的特点，提出了基于骑手优化的动态布谷鸟搜索策略（rider optimization cuckoo search，ROCS）。结合骑手优化算法（rider optimization algorithm，ROA）思想，利用多种群在单周期内进行多策略寻优，动态使用最优策略进行加强搜索，提高算法对复杂问题的收敛效率；同时对Lévy飞行运动进行动态参数调节，改善算法搜索前期及末期表现。仿真测试结果显示，改进算法对复杂问题的优化表现优于比对算法，算法寻优效率得到显著改善。

Dynamic cuckoo search algorithm based on rider optimization

Aiming at the characteristics of the traditional cuckoo search algorithm with low convergence accuracy and large limitation of iterative step for complex problems, this paper proposed a dynamic cuckoo search strategy based on rider optimization. The algorithm combined with the idea of rider optimization algorithm, using multiple groups and optimal strategies in a single cycle. The optimal strategy could dynamically enhance the searching and improve the convergence efficiency of the algorithm for complex problems. At the same time, this algorithm adjusted the dynamic parameters of Lévy flight motion to improve the early and late performance of the algorithm search. The simulation test results show that the optimization performance of the improved algorithm ROCS is better than the comparison algorithm for complex problems, and the optimization efficiency of the algorithm has been significantly improved.