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The sine-cosine algorithm (SCA) is a new population-based meta-heuristicalgorithm. In addition to exploiting sine and cosine functions to perform localand global searches (hence the name sine-cosine), the SCA introduces severalrandom and adaptive parameters to facilitate the search process. Although itshows promising results, the search process of the SCA is vulnerable to localminima/maxima due to the adoption of a fixed switch probability and the boundedmagnitude of the sine and cosine functions (from -1 to 1). In this paper, wepropose a new hybrid Q-learning sine-cosine- based strategy, called theQ-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate theswitching probability. Instead, we rely on the Q-learning algorithm (based onthe penalty and reward mechanism) to dynamically identify the best operationduring runtime. Additionally, we integrate two new operations (L\'evy flightmotion and crossover) into the QLSCA to facilitate jumping out of localminima/maxima and enhance the solution diversity. To assess its performance, weadopt the QLSCA for the combinatorial test suite minimization problem.Experimental results reveal that the QLSCA is statistically superior withregard to test suite size reduction compared to recent state-of-the-artstrategies, including the original SCA, the particle swarm test generator(PSTG), adaptive particle swarm optimization (APSO) and the cuckoo searchstrategy (CS) at the 95% confidence level. However, concerning the comparisonwith discrete particle swarm optimization (DPSO), there is no significantdifference in performance at the 95% confidence level. On a positive note, theQLSCA statistically outperforms the DPSO in certain configurations at the 90%confidence level.

A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem