Flower pollination algorithm for data generation and analytics - a diagnostic analysis
The effectiveness of optimization in scientific and engineering applications has made optimization a popular area of scientific investigation in data generation and analytics leading to the design of several optimization algorithms. In view of the huge number of optimization algorithms in literature...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2020
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| Online Access: | https://umpir.ump.edu.my/id/eprint/29330/ |
| _version_ | 1848827275227693056 |
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| author | Odili, Julius Beneoluchi Noraziah, Ahmad Babalola, Asegunloluwa Eunice |
| author_facet | Odili, Julius Beneoluchi Noraziah, Ahmad Babalola, Asegunloluwa Eunice |
| author_sort | Odili, Julius Beneoluchi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The effectiveness of optimization in scientific and engineering applications has made optimization a popular area of scientific investigation in data generation and analytics leading to the design of several optimization algorithms. In view of the huge number of optimization algorithms in literature, there is the need for a thorough diagnostic evaluation so as to bring out the strengths and weaknesses of each technique: that way, assist researchers in making informed choices whenever they are confronted with an optimization problem. This paper aims to fill the gap in literature of the Flower Pollination Algorithm in terms of diagnostic assessment of the impact of the number of iteration and search agents in solving the popular benchmark Sphere function and the unpopular but complex multimodal Dejong 5 function, otherwise called Shekel Foxhole function. After a number of empirical evaluations, the study finds out that the Flower Pollination Algorithm is not only a fast technique but also obtained good results when the appropriate iteration and flower population is used. |
| first_indexed | 2025-11-15T03:58:07Z |
| format | Article |
| id | ump-29330 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:07Z |
| publishDate | 2020 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-293302025-10-02T03:09:24Z https://umpir.ump.edu.my/id/eprint/29330/ Flower pollination algorithm for data generation and analytics - a diagnostic analysis Odili, Julius Beneoluchi Noraziah, Ahmad Babalola, Asegunloluwa Eunice QA76 Computer software The effectiveness of optimization in scientific and engineering applications has made optimization a popular area of scientific investigation in data generation and analytics leading to the design of several optimization algorithms. In view of the huge number of optimization algorithms in literature, there is the need for a thorough diagnostic evaluation so as to bring out the strengths and weaknesses of each technique: that way, assist researchers in making informed choices whenever they are confronted with an optimization problem. This paper aims to fill the gap in literature of the Flower Pollination Algorithm in terms of diagnostic assessment of the impact of the number of iteration and search agents in solving the popular benchmark Sphere function and the unpopular but complex multimodal Dejong 5 function, otherwise called Shekel Foxhole function. After a number of empirical evaluations, the study finds out that the Flower Pollination Algorithm is not only a fast technique but also obtained good results when the appropriate iteration and flower population is used. Elsevier B.V. 2020 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/29330/1/14.%20Flower%20pollination%20algorithm%20for%20data%20generation%20and%20analytics%20-%20a%20diagnostic%20analysis.pdf Odili, Julius Beneoluchi and Noraziah, Ahmad and Babalola, Asegunloluwa Eunice (2020) Flower pollination algorithm for data generation and analytics - a diagnostic analysis. Scientific African, 8 (e00440). pp. 1-9. ISSN 2468-2276. (Published) https://doi.org/10.1016/j.sciaf.2020.e00440 https://doi.org/10.1016/j.sciaf.2020.e00440 https://doi.org/10.1016/j.sciaf.2020.e00440 |
| spellingShingle | QA76 Computer software Odili, Julius Beneoluchi Noraziah, Ahmad Babalola, Asegunloluwa Eunice Flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| title | Flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| title_full | Flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| title_fullStr | Flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| title_full_unstemmed | Flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| title_short | Flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| title_sort | flower pollination algorithm for data generation and analytics - a diagnostic analysis |
| topic | QA76 Computer software |
| url | https://umpir.ump.edu.my/id/eprint/29330/ https://umpir.ump.edu.my/id/eprint/29330/ https://umpir.ump.edu.my/id/eprint/29330/ |