Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach

He, Xingshi, Yang, Xin-She ORCID logoORCID: https://orcid.org/0000-0001-8231-5556, Karamanoglu, Mehmet ORCID logoORCID: https://orcid.org/0000-0002-5049-2993 and Zhao, Yuxin (2017) Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach. Procedia Computer Science, Volume 108. In: International Conference on Computational Science, ICCS 2017, 12-14 Jun 2017, Zurich, Switzerland. . ISSN 1877-0509 [Conference or Workshop Item] (doi:10.1016/j.procs.2017.05.020)

[img]
Preview
PDF - Published version (with publisher's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0.

Download (475kB) | Preview

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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 22008
Notes on copyright: Copyright: © 2017 The Author(s). Published by Elsevier B.V.
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 15 Jun 2017 09:58
Last Modified: 29 Nov 2022 21:20
URI: https://eprints.mdx.ac.uk/id/eprint/22008

Actions (login required)

View Item View Item

Statistics

Activity Overview
6 month trend
327Downloads
6 month trend
367Hits

Additional statistics are available via IRStats2.