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International Conference on Unconventional Computing and Natural Computation

UCNC 2012: Unconventional Computation and Natural Computation pp 240–249Cite as

Flower Pollination Algorithm for Global Optimization

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Part of the Lecture Notes in Computer Science book series (LNTCS,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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Author information

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK

    Xin-She Yang

Authors
  1. Xin-She Yang
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Editor information

Editors and Affiliations

  1. Laboratoire d’Informatique, Fondamentale d’Orléan et Départment d’informatique, UFR Sciences, Université d’Orléans, 45067, Orléans Cedex 2, France

    Jérôme Durand-Lose

  2. Department of Mathematics and Statistics, University of South Florida, 33620, Tampa, FL, USA

    Nataša Jonoska

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Yang, XS. (2012). Flower Pollination Algorithm for Global Optimization. In: Durand-Lose, J., Jonoska, N. (eds) Unconventional Computation and Natural Computation. UCNC 2012. Lecture Notes in Computer Science, vol 7445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32894-7_27

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  • DOI: https://doi.org/10.1007/978-3-642-32894-7_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32893-0

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