Flower pollination algorithm parameters tuning

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...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 25; no. 22; pp. 14429 - 14447
Main Authors: Mergos, Panagiotis E., Yang, Xin-She
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computational Intelligence
Computing costs
Control
Engineering
Genetic algorithms
Heuristic methods
Mathematical Logic and Foundations
Mechatronics
Optimization
Optimization algorithms
Parameter identification
Pollinators
Robotics
Traveling salesman problem
Tuning
Parameters tuning
Metaheuristics
Evolutionary
Optimization
Flower pollination algorithm
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary: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.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-06230-1