An adaptive opposition-based learning selection: The case for jaya algorithm
Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31908/1/An%20adaptive%20opposition-based%20learning%20selection.pdf |
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| author | Nasser, Abdullah B. Kamal Z., Zamli Hujainah, Fadhl Ghanem, Waheed Ali H. M. Saad, Abdul-Malik H. Y. Mohammed Alduais, Nayef Abdulwahab |
| author_facet | Nasser, Abdullah B. Kamal Z., Zamli Hujainah, Fadhl Ghanem, Waheed Ali H. M. Saad, Abdul-Malik H. Y. Mohammed Alduais, Nayef Abdulwahab |
| author_sort | Nasser, Abdullah B. |
| collection | UMP |
| description | Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process. |
| first_indexed | 2024-03-06T12:51:33Z |
| format | Article |
| id | UMPir31908 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:51:33Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir319082022-02-11T07:34:48Z http://umpir.ump.edu.my/id/eprint/31908/ An adaptive opposition-based learning selection: The case for jaya algorithm Nasser, Abdullah B. Kamal Z., Zamli Hujainah, Fadhl Ghanem, Waheed Ali H. M. Saad, Abdul-Malik H. Y. Mohammed Alduais, Nayef Abdulwahab QA76 Computer software Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process. IEEE 2021-01-28 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/31908/1/An%20adaptive%20opposition-based%20learning%20selection.pdf Nasser, Abdullah B. and Kamal Z., Zamli and Hujainah, Fadhl and Ghanem, Waheed Ali H. M. and Saad, Abdul-Malik H. Y. and Mohammed Alduais, Nayef Abdulwahab (2021) An adaptive opposition-based learning selection: The case for jaya algorithm. IEEE Access, 9 (9337859). 55581 -55594. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2021.3055367 https://doi.org/10.1109/ACCESS.2021.3055367 |
| spellingShingle | QA76 Computer software Nasser, Abdullah B. Kamal Z., Zamli Hujainah, Fadhl Ghanem, Waheed Ali H. M. Saad, Abdul-Malik H. Y. Mohammed Alduais, Nayef Abdulwahab An adaptive opposition-based learning selection: The case for jaya algorithm |
| title | An adaptive opposition-based learning selection: The case for jaya algorithm |
| title_full | An adaptive opposition-based learning selection: The case for jaya algorithm |
| title_fullStr | An adaptive opposition-based learning selection: The case for jaya algorithm |
| title_full_unstemmed | An adaptive opposition-based learning selection: The case for jaya algorithm |
| title_short | An adaptive opposition-based learning selection: The case for jaya algorithm |
| title_sort | adaptive opposition based learning selection the case for jaya algorithm |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/31908/1/An%20adaptive%20opposition-based%20learning%20selection.pdf |
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