Flower Pollination Algorithm for Global Optimization

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.

[1]  Germán Terrazas,et al.  Nature Inspired Cooperative Strategies for Optimization, NICSO 2010, May 12-14, 2010, Granada, Spain , 2012, NISCO.

[2]  Beverley J. Glover,et al.  Understanding flowers and flowering : an integrated approach , 2007 .

[3]  Carlos A. Coello Coello,et al.  Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer , 2008, Informatica.

[4]  N. Waser Flower Constancy: Definition, Cause, and Measurement , 1986, The American Naturalist.

[5]  L. Chittka,et al.  Flower Constancy, Insect Psychology, and Plant Evolution , 1999, Naturwissenschaften.

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Caro Lucas,et al.  Swarm Clustering Based on Flowers Pollination by Artificial Bees , 2006, Swarm Intelligence in Data Mining.

[8]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[9]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[10]  A. Reynolds,et al.  Free-Flight Odor Tracking in Drosophila Is Consistent with an Optimal Intermittent Scale-Free Search , 2007, PloS one.

[11]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[12]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[13]  Ilya Pavlyukevich Lévy flights, non-local search and simulated annealing , 2007, J. Comput. Phys..

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[16]  Ajith Abraham,et al.  Swarm Intelligence in Data Mining , 2009, Swarm Intelligence in Data Mining.

[17]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[18]  D. Ackley A connectionist machine for genetic hillclimbing , 1987 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Li Cheng,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010 .

[21]  B. Glover Understanding Flowers and Flowering , 2007 .

[22]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.