Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).

尽管Cuckoo Search（CS）是一种受自然启发的新启发式元启发式优化算法，但由于已被证明在解决复杂的非线性问题方面非常有效，因此它已被广泛应用于工程应用中。在本文中，对原始CS算法进行了有效的修改，以提高其效率和鲁棒性。更具体地说，该算法的常数参数，例如宿主鸟发现外来卵的概率和Levy飞行的步长已被动态调整。此外，在优化公式中引入了静态和动态惩罚函数。最后，开发了一种混合优化方法，将CS的优势与Bird Swarm算法（BSA）的优势相结合。在相关研究中广泛使用的基准问题，