Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach
Conference paper
He, X., Yang, X., Karamanoglu, M. and Zhao, Y. 2017. Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach. International Conference on Computational Science, ICCS 2017. Zurich, Switzerland 12 - 14 Jun 2017 Elsevier. https://doi.org/10.1016/j.procs.2017.05.020
Type | Conference paper |
---|---|
Title | Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach |
Authors | He, X., Yang, X., Karamanoglu, M. and Zhao, Y. |
Abstract | Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently. |
Keywords | Algorithm; convergence analysis; flower pollination algorithm; Levy flight; metaheuristics; nature-inspired strategy; optimization |
Conference | International Conference on Computational Science, ICCS 2017 |
Proceedings Title | Procedia Computer Science |
ISSN | 1877-0509 |
Publisher | Elsevier |
Publication dates | |
Online | 09 Jun 2017 |
2017 | |
Publication process dates | |
Deposited | 15 Jun 2017 |
Accepted | 20 Mar 2017 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | Copyright: © 2017 The Author(s). Published by Elsevier B.V. |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.procs.2017.05.020 |
Web of Science identifier | WOS:000404959000139 |
Language | English |
https://repository.mdx.ac.uk/item/87049
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