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An Adaptive Opposition-based Learning Selection: The Case for Jaya Algorithm

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Název: An Adaptive Opposition-based Learning Selection: The Case for Jaya Algorithm
Autoři: Nasser, Abdullah B., Zamli, Kamal Z., Hujainah, Fadhl Mohammad Omar, 1987, Ghanem, Waheed Ali H.M., Saad, Abdul Malik H.Y., Alduais, Nayef Abdulwahab Mohammed
Zdroj: IEEE Access. 9:55581-55594
Témata: Jaya Algorithm, Opposition based Learning, Artificial neural networks, Reinforcement learning, Adaptive Selection, Optimization, Software algorithms, Genetic algorithms, Explosions, Search problems
Popis: 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.
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Přístupová URL adresa: https://research.chalmers.se/publication/522316
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  Data: An Adaptive Opposition-based Learning Selection: The Case for Jaya Algorithm
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Nasser%2C+Abdullah+B%2E%22">Nasser, Abdullah B.</searchLink><br /><searchLink fieldCode="AR" term="%22Zamli%2C+Kamal+Z%2E%22">Zamli, Kamal Z.</searchLink><br /><searchLink fieldCode="AR" term="%22Hujainah%2C+Fadhl+Mohammad+Omar%22">Hujainah, Fadhl Mohammad Omar</searchLink>, 1987<br /><searchLink fieldCode="AR" term="%22Ghanem%2C+Waheed+Ali+H%2EM%2E%22">Ghanem, Waheed Ali H.M.</searchLink><br /><searchLink fieldCode="AR" term="%22Saad%2C+Abdul+Malik+H%2EY%2E%22">Saad, Abdul Malik H.Y.</searchLink><br /><searchLink fieldCode="AR" term="%22Alduais%2C+Nayef+Abdulwahab+Mohammed%22">Alduais, Nayef Abdulwahab Mohammed</searchLink>
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  Data: <i>IEEE Access</i>. 9:55581-55594
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  Data: <searchLink fieldCode="DE" term="%22Jaya+Algorithm%22">Jaya Algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Opposition+based+Learning%22">Opposition based Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+Selection%22">Adaptive Selection</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization%22">Optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Software+algorithms%22">Software algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Explosions%22">Explosions</searchLink><br /><searchLink fieldCode="DE" term="%22Search+problems%22">Search problems</searchLink>
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  Label: Description
  Group: Ab
  Data: 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.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1109/ACCESS.2021.3055367
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 55581
    Subjects:
      – SubjectFull: Jaya Algorithm
        Type: general
      – SubjectFull: Opposition based Learning
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Adaptive Selection
        Type: general
      – SubjectFull: Optimization
        Type: general
      – SubjectFull: Software algorithms
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Explosions
        Type: general
      – SubjectFull: Search problems
        Type: general
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      – TitleFull: An Adaptive Opposition-based Learning Selection: The Case for Jaya Algorithm
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            NameFull: Nasser, Abdullah B.
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            NameFull: Zamli, Kamal Z.
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            NameFull: Ghanem, Waheed Ali H.M.
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            NameFull: Saad, Abdul Malik H.Y.
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            NameFull: Alduais, Nayef Abdulwahab Mohammed
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              M: 01
              Type: published
              Y: 2021
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