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Flower Pollination Algorithm for Global Optimization

  • Xin-She Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7445)

Abstract

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.

Keywords

Genetic Algorithm Particle Swarm Optimization Global Minimum Multiobjective Optimization Local Pollination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xin-She Yang
    • 1
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK

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