Flower pollination algorithm parameters tuning
Article
Mergos, P. and Yang, X. 2021. Flower pollination algorithm parameters tuning. Soft Computing. 25 (22), pp. 14429-14447. https://doi.org/10.1007/s00500-021-06230-1
Type | Article |
---|---|
Title | Flower pollination algorithm parameters tuning |
Authors | Mergos, P. and Yang, X. |
Abstract | The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost. [Abstract copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.] |
Keywords | Optimization; Metaheuristics; Evolutionary; Flower pollination algorithm; Parameters tuning |
Publisher | Springer |
Journal | Soft Computing |
ISSN | 1432-7643 |
Electronic | 1433-7479 |
Publication dates | |
Online | 12 Sep 2021 |
Nov 2021 | |
Publication process dates | |
Deposited | 06 Oct 2021 |
Accepted | 31 Aug 2021 |
Output status | Published |
Accepted author manuscript | File Access Level Restricted |
Accepted author manuscript | File Access Level Open |
Copyright Statement | This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-ma...), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00500-021-06230-1 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00500-021-06230-1 |
Web of Science identifier | WOS:000695083500003 |
Language | English |
https://repository.mdx.ac.uk/item/89841
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