  Flower pollination is an intriguing process in the natural world. Its
evolutionary characteristics can be used to design new optimization algorithms.
In this paper, we propose a new algorithm, namely, flower pollination
algorithm, inspired by the pollination process of flowers. We first use ten
test functions to validate the new algorithm, and compare its performance with
genetic algorithms and particle swarm optimization. Our simulation results show
the flower algorithm is more efficient than both GA and PSO. We also use the
flower algorithm to solve a nonlinear design benchmark, which shows the
convergence rate is almost exponential.
