The paper introduces the Flower Pollination Algorithm (FPA), a new optimization algorithm inspired by the pollination process of flowers. The authors first review the characteristics of flower pollination, including biotic and abiotic pollination, flower constancy, and the role of pollinators. They then develop FPA based on these characteristics, defining four key rules: global and local pollination, flower constancy, and a switch probability for switching between global and local pollination. The algorithm is validated using ten test functions and compared with genetic algorithms (GA) and particle swarm optimization (PSO). The results show that FPA outperforms both GA and PSO in terms of efficiency and convergence rate. Additionally, FPA is applied to a nonlinear design benchmark, demonstrating its ability to find optimal solutions quickly. The paper concludes by discussing potential extensions of the algorithm, such as incorporating multiple pollen gametes and flowers, and extending it to discrete problems for combinatorial optimization.The paper introduces the Flower Pollination Algorithm (FPA), a new optimization algorithm inspired by the pollination process of flowers. The authors first review the characteristics of flower pollination, including biotic and abiotic pollination, flower constancy, and the role of pollinators. They then develop FPA based on these characteristics, defining four key rules: global and local pollination, flower constancy, and a switch probability for switching between global and local pollination. The algorithm is validated using ten test functions and compared with genetic algorithms (GA) and particle swarm optimization (PSO). The results show that FPA outperforms both GA and PSO in terms of efficiency and convergence rate. Additionally, FPA is applied to a nonlinear design benchmark, demonstrating its ability to find optimal solutions quickly. The paper concludes by discussing potential extensions of the algorithm, such as incorporating multiple pollen gametes and flowers, and extending it to discrete problems for combinatorial optimization.