Paper Info
Title
Flower pollination algorithm parameters tuning
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.
Year
DOI
Venue
2021
10.1007/s00500-021-06230-1
SOFT COMPUTING
Keywords
DocType
Volume
Optimization, Metaheuristics, Evolutionary, Flower pollination algorithm, Parameters tuning
Journal
25
Issue
ISSN
Citations 
22
1432-7643
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Panagiotis E Mergos100.34
Xin-She Yang2342.59