An Adaptive Opposition-based Learning Selection: The Case for Jaya Algorithm
Artikel i vetenskaplig tidskrift, 2021

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.

Jaya Algorithm

Opposition based Learning

Artificial neural networks

Reinforcement learning

Adaptive Selection


Software algorithms

Genetic algorithms


Search problems


Abdullah B. Nasser

Universiti Malaysia Pahang

Kamal Z. Zamli

Universiti Malaysia Pahang

Fadhl Mohammad Omar Hujainah

Chalmers, Data- och informationsteknik, Software Engineering

Waheed Ali H.M. Ghanem

University Malaysia Terengganu

Abdul Malik H.Y. Saad

Universiti Teknologi Malaysia

Nayef Abdulwahab Mohammed Alduais

Universiti Tun Hussein Onn Malaysia

Universiti Teknologi Malaysia

IEEE Access

2169-3536 (ISSN) 21693536 (eISSN)

Vol. 9 55581-55594 9337859



Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)



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