This paper introduces a new metaheuristic algorithm called Cuckoo Search (CS), which is inspired by the brood parasitic behavior of some cuckoo species and the Lévy flight behavior of birds and fruit flies. The algorithm is designed to solve optimization problems and is validated against various test functions. The authors compare CS with genetic algorithms (GA) and particle swarm optimization (PSO), demonstrating that CS outperforms these algorithms in terms of efficiency and success rate in finding global optima. The key features of CS include its population-based approach, elitism, and efficient randomization through Lévy flights. The paper also discusses the implications of the results and suggests further research directions, such as parameter studies and hybridization with other algorithms.This paper introduces a new metaheuristic algorithm called Cuckoo Search (CS), which is inspired by the brood parasitic behavior of some cuckoo species and the Lévy flight behavior of birds and fruit flies. The algorithm is designed to solve optimization problems and is validated against various test functions. The authors compare CS with genetic algorithms (GA) and particle swarm optimization (PSO), demonstrating that CS outperforms these algorithms in terms of efficiency and success rate in finding global optima. The key features of CS include its population-based approach, elitism, and efficient randomization through Lévy flights. The paper also discusses the implications of the results and suggests further research directions, such as parameter studies and hybridization with other algorithms.