Flower Pollination Algorithm for Global Optimization
This work addresses optimization problems in computational fields, but it is incremental as it adapts a known natural process into a new algorithm.
The authors tackled global optimization by proposing a flower pollination algorithm inspired by natural pollination processes, and simulation results showed it is more efficient than genetic algorithms and particle swarm optimization, with a convergence rate nearly exponential for a nonlinear design benchmark.
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.