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An Adaptive Opposit...
An Adaptive Opposition-based Learning Selection: The Case for Jaya Algorithm
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Nasser, Abdullah B. (författare)
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Zamli, Kamal Z. (författare)
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- Hujainah, Fadhl Mohammad Omar, 1987 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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Ghanem, Waheed Ali H.M. (författare)
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- Saad, Abdul Malik H.Y. (författare)
- Universiti Teknologi Malaysia
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- Alduais, Nayef Abdulwahab Mohammed (författare)
- Universiti Tun Hussein Onn Malaysia,Universiti Teknologi Malaysia
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(creator_code:org_t)
- 2021
- 2021
- Engelska.
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 9, s. 55581-55594
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Jaya Algorithm
- Opposition based Learning
- Artificial neural networks
- Reinforcement learning
- Adaptive Selection
- Optimization
- Software algorithms
- Genetic algorithms
- Explosions
- Search problems
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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