Improved Cuckoo Search algorithmic variants for constrained nonlinear optimizationImproved Cuckoo Search algorithmic variants for constrained nonlinear optimization Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2021-12-242020enAlthough 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.).http://creativecommons.org/licenses/by/4.0/Advances in Engineering Software149 Tsipianitis Alexandros Τσιπιανιτης Αλεξανδρος Tsompanakis Ioannis Τσομπανακης Ιωαννης Elsevier Structural optimization Nonlinear constraints Dynamic penalty Swarm intelligence Evolutionary optimizers Cuckoo Search (CS) Benchmark problems