A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem
To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightfor...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/33667/ http://umpir.ump.edu.my/id/eprint/33667/1/A%20Modified%20Symbiotic%20Organism%20Search.pdf |
| _version_ | 1848824314330087424 |
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| author | Nurul Asyikin, Zainal Kamal Z., Zamli Fakhrud, Din |
| author_facet | Nurul Asyikin, Zainal Kamal Z., Zamli Fakhrud, Din |
| author_sort | Nurul Asyikin, Zainal |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results. |
| first_indexed | 2025-11-15T03:11:04Z |
| format | Conference or Workshop Item |
| id | ump-33667 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:11:04Z |
| publishDate | 2020 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-336672022-04-11T03:04:34Z http://umpir.ump.edu.my/id/eprint/33667/ A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem Nurul Asyikin, Zainal Kamal Z., Zamli Fakhrud, Din QA Mathematics To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results. Springer 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33667/1/A%20Modified%20Symbiotic%20Organism%20Search.pdf Nurul Asyikin, Zainal and Kamal Z., Zamli and Fakhrud, Din (2020) A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem. In: InECCE2019: Proceedings of the 5th International Conference on Electrical, Control & Computer Engineering , 29th July 2019 , Kuantan, Pahang, Malaysia. pp. 219-229., 632. ISBN 978-981-15-2317-5 (Published) https://doi.org/10.1007/978-981-15-2317-5_19 |
| spellingShingle | QA Mathematics Nurul Asyikin, Zainal Kamal Z., Zamli Fakhrud, Din A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem |
| title | A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem |
| title_full | A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem |
| title_fullStr | A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem |
| title_full_unstemmed | A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem |
| title_short | A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem |
| title_sort | modified symbiotic organism search algorithm with lévy flight for software module clustering problem |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/33667/ http://umpir.ump.edu.my/id/eprint/33667/ http://umpir.ump.edu.my/id/eprint/33667/1/A%20Modified%20Symbiotic%20Organism%20Search.pdf |