This paper introduces a new metaheuristic algorithm called Cuckoo Search (CS), inspired by the brood parasitism behavior of some cuckoo species and the Lévy flight behavior of birds and fruit flies. The algorithm is designed to solve optimization problems by simulating the natural behaviors of cuckoos and Lévy flights. The CS algorithm is validated against test functions and compared with genetic algorithms (GA) and particle swarm optimization (PSO). The results show that CS outperforms these algorithms in terms of efficiency and success rate in finding global optima. The algorithm is based on three idealized rules: each cuckoo lays one egg in a randomly chosen nest; the best nests with high-quality eggs are carried over to the next generation; and the number of available host nests is fixed, with a probability $ p_a $ of discovering a cuckoo's egg and replacing the nest. The algorithm uses Lévy flights to generate new solutions, which are more efficient in exploring the search space due to their long step lengths. The Lévy flight behavior is characterized by a power-law step-length distribution with a heavy tail, allowing for both local and global search capabilities. The CS algorithm is implemented using a population-based approach, with a fixed number of nests and a probability $ p_a $ for nest replacement. The algorithm is tested on various benchmark functions, including De Jong's first function, Easom's function, Shubert's function, Griewangk's function, Ackley's function, generalized Rosenbrock's function, Schwefel's function, Rastrigin's function, and Michalewicz's function. The results show that CS outperforms GA and PSO in terms of convergence rate and success rate in finding global optima. The CS algorithm has fewer parameters to tune compared to GA and PSO, making it more generic and robust for a wide range of optimization problems. The algorithm is also capable of handling multimodal and multiobjective optimization problems. The paper concludes that CS is a promising optimization strategy that can be extended to various applications, including multiobjective optimization with constraints and NP-hard problems. Further research is needed to explore the sensitivity of the algorithm and its potential for hybridization with other popular algorithms.This paper introduces a new metaheuristic algorithm called Cuckoo Search (CS), inspired by the brood parasitism behavior of some cuckoo species and the Lévy flight behavior of birds and fruit flies. The algorithm is designed to solve optimization problems by simulating the natural behaviors of cuckoos and Lévy flights. The CS algorithm is validated against test functions and compared with genetic algorithms (GA) and particle swarm optimization (PSO). The results show that CS outperforms these algorithms in terms of efficiency and success rate in finding global optima. The algorithm is based on three idealized rules: each cuckoo lays one egg in a randomly chosen nest; the best nests with high-quality eggs are carried over to the next generation; and the number of available host nests is fixed, with a probability $ p_a $ of discovering a cuckoo's egg and replacing the nest. The algorithm uses Lévy flights to generate new solutions, which are more efficient in exploring the search space due to their long step lengths. The Lévy flight behavior is characterized by a power-law step-length distribution with a heavy tail, allowing for both local and global search capabilities. The CS algorithm is implemented using a population-based approach, with a fixed number of nests and a probability $ p_a $ for nest replacement. The algorithm is tested on various benchmark functions, including De Jong's first function, Easom's function, Shubert's function, Griewangk's function, Ackley's function, generalized Rosenbrock's function, Schwefel's function, Rastrigin's function, and Michalewicz's function. The results show that CS outperforms GA and PSO in terms of convergence rate and success rate in finding global optima. The CS algorithm has fewer parameters to tune compared to GA and PSO, making it more generic and robust for a wide range of optimization problems. The algorithm is also capable of handling multimodal and multiobjective optimization problems. The paper concludes that CS is a promising optimization strategy that can be extended to various applications, including multiobjective optimization with constraints and NP-hard problems. Further research is needed to explore the sensitivity of the algorithm and its potential for hybridization with other popular algorithms.