The Flower Pollination Algorithm (FPA) is a nature-inspired optimization algorithm based on the pollination process of flowering plants. Inspired by the pollination behavior of flowers, the algorithm mimics the global and local pollination processes, including the movement of pollinators and flower constancy. The algorithm uses a combination of global pollination, which involves long-distance movement of pollinators, and local pollination, which involves short-distance interactions between flowers. A switch probability determines the balance between these two processes. The algorithm is designed to solve complex optimization problems by simulating the pollination process. It uses a Lévy flight distribution to model the long-distance movement of pollinators and a uniform distribution for local pollination. The algorithm is validated using a set of test functions and a nonlinear design benchmark. The results show that the FPA outperforms genetic algorithms (GA) and particle swarm optimization (PSO) in terms of efficiency and convergence rate. The FPA is particularly effective in solving nonlinear optimization problems, with an exponential convergence rate. The algorithm is applied to a pressure vessel design problem, where it successfully finds the optimal solution, matching results obtained by other optimization methods. The FPA's performance is further demonstrated through simulations, showing its ability to converge to the optimal solution quickly and efficiently. The FPA is a promising metaheuristic algorithm that can be extended to various applications, including multiobjective optimization and combinatorial optimization. Future research could explore more complex forms of flower constancy and discrete versions of the algorithm for solving different types of optimization problems. The algorithm's efficiency and adaptability make it a valuable tool in the field of optimization.The Flower Pollination Algorithm (FPA) is a nature-inspired optimization algorithm based on the pollination process of flowering plants. Inspired by the pollination behavior of flowers, the algorithm mimics the global and local pollination processes, including the movement of pollinators and flower constancy. The algorithm uses a combination of global pollination, which involves long-distance movement of pollinators, and local pollination, which involves short-distance interactions between flowers. A switch probability determines the balance between these two processes. The algorithm is designed to solve complex optimization problems by simulating the pollination process. It uses a Lévy flight distribution to model the long-distance movement of pollinators and a uniform distribution for local pollination. The algorithm is validated using a set of test functions and a nonlinear design benchmark. The results show that the FPA outperforms genetic algorithms (GA) and particle swarm optimization (PSO) in terms of efficiency and convergence rate. The FPA is particularly effective in solving nonlinear optimization problems, with an exponential convergence rate. The algorithm is applied to a pressure vessel design problem, where it successfully finds the optimal solution, matching results obtained by other optimization methods. The FPA's performance is further demonstrated through simulations, showing its ability to converge to the optimal solution quickly and efficiently. The FPA is a promising metaheuristic algorithm that can be extended to various applications, including multiobjective optimization and combinatorial optimization. Future research could explore more complex forms of flower constancy and discrete versions of the algorithm for solving different types of optimization problems. The algorithm's efficiency and adaptability make it a valuable tool in the field of optimization.