Cuckoo search (CS) is a nature-inspired metaheuristic algorithm developed by Xin-She Yang and Suash Deb in 2009. It is efficient in solving global optimization problems and has been applied in various fields. The algorithm is based on the brood parasitism behavior of some cuckoo species and incorporates Lévy flights for global exploration. CS is known for its ability to find global optima efficiently and has been shown to outperform other algorithms like PSO and genetic algorithms in many applications. The algorithm uses a combination of local and global search strategies, controlled by a switching probability. It employs a random walk with Lévy flights to explore the search space effectively, which allows it to avoid local optima and find better solutions. CS has been applied in engineering design, data fusion, wireless sensor networks, neural network training, and optimization of complex systems. Recent studies have demonstrated its effectiveness in solving a wide range of optimization problems, including multi-objective optimization and structural design. CS has also been extended to handle discrete problems and combined with other techniques like quantum-inspired approaches for solving Knapsack problems. Despite its success, there are still challenges in understanding the theoretical foundations of CS and optimizing its parameters for different applications. Future research aims to improve the algorithm's efficiency, robustness, and applicability to larger-scale problems. Overall, CS is a promising optimization method with a wide range of applications in science and engineering.Cuckoo search (CS) is a nature-inspired metaheuristic algorithm developed by Xin-She Yang and Suash Deb in 2009. It is efficient in solving global optimization problems and has been applied in various fields. The algorithm is based on the brood parasitism behavior of some cuckoo species and incorporates Lévy flights for global exploration. CS is known for its ability to find global optima efficiently and has been shown to outperform other algorithms like PSO and genetic algorithms in many applications. The algorithm uses a combination of local and global search strategies, controlled by a switching probability. It employs a random walk with Lévy flights to explore the search space effectively, which allows it to avoid local optima and find better solutions. CS has been applied in engineering design, data fusion, wireless sensor networks, neural network training, and optimization of complex systems. Recent studies have demonstrated its effectiveness in solving a wide range of optimization problems, including multi-objective optimization and structural design. CS has also been extended to handle discrete problems and combined with other techniques like quantum-inspired approaches for solving Knapsack problems. Despite its success, there are still challenges in understanding the theoretical foundations of CS and optimizing its parameters for different applications. Future research aims to improve the algorithm's efficiency, robustness, and applicability to larger-scale problems. Overall, CS is a promising optimization method with a wide range of applications in science and engineering.