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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01.11.2021
Springer Nature B.V |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Yang, Xin-She Mergos, Panagiotis E. |
| Author_xml | – sequence: 1 givenname: Panagiotis E. orcidid: 0000-0003-3817-9520 surname: Mergos fullname: Mergos, Panagiotis E. email: panagiotis.mergos.1@city.ac.uk organization: Department of Civil Engineering, City, University of London, Structural Engineering, Research Centre for Civil Engineering Structures, City, University of London – sequence: 2 givenname: Xin-She surname: Yang fullname: Yang, Xin-She organization: Design Engineering and Mathematics, Middlesex University London |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34539232$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s00521-013-1498-4 10.5815/ijmecs.2014.03.05 10.1016/j.cie.2021.107250 10.1016/j.renene.2015.04.034 10.1007/s00521-017-3176-4 10.3390/su8030235 10.1016/j.energy.2016.02.041 10.1016/j.jcp.2007.06.008 10.1007/978-0-387-30164-8_630 10.1007/s00366-011-0241-y 10.1007/s00521-016-2524-0 10.1007/s10462-018-9624-4 10.1016/j.ijepes.2015.11.093 10.1007/978-3-319-13826-8_5 10.1007/978-981-10-3728-3_22 10.1007/s00521-020-05296-6 10.1016/j.ipl.2015.08.007 10.18576/isl/050104 10.1007/s11227-019-02776-y 10.1515/aee-2016-0014 10.1007/978-3-319-26245-1_2 10.1007/s00521-017-3313-0 10.1093/acprof:oso/9780198565970.001.0001 10.1109/ICITEED.2016.7863285 10.1016/j.asoc.2015.05.015 10.1080/0305215X.2013.832237 10.1016/j.swevo.2011.02.001 10.1007/s00500-017-2744-y 10.1016/j.asoc.2015.08.037 10.1504/IJBIC.2010.032124 10.1007/s00158-019-02380-x 10.1016/j.neucom.2015.01.110 |
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| Keywords | Parameters tuning Metaheuristics Evolutionary Optimization Flower pollination algorithm |
| Language | English |
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| SubjectTerms | 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 |
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| Title | Flower pollination algorithm parameters tuning |
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