A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

PloS One
Kamal Z ZamliMiroslav Bures

Abstract

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size red...Continue Reading

References

Oct 27, 2004·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Maozu GuoJacek Malec
May 13, 1983·Science·S KirkpatrickM P Vecchi
Jul 16, 2010·Current Opinion in Structural Biology·Alexis Vallée-Bélisle, Kevin W Plaxco

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Citations

Jan 12, 2021·Annals of Operations Research·Soheyl Khalilpourazari, Hossein Hashemi Doulabi
Aug 7, 2021·PloS One·Muhammad Zubair RehmanNazri Mohd Nawi
May 4, 2020··Md. Abdul KaderKamal Z. Zamli
May 4, 2020··Kamal Z. ZamliMd. Abdul Kader

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