text: 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 (Le´vy 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 reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95 confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95 confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90 confidence level.
  @cite_30: Context: Although useful, AI-based variable strength t-way strategies are lacking in terms of the support for high interaction strength. Additionally, most AI-based strategies generally do not address the support for constraints. Addressing the aforementioned issues, this paper elaborates the design, implementation, and evaluation of a novel variable-strength-based on harmony search algorithm, called Harmony Search Strategy (HSS). Objective: The objective of this work is to investigate the adoption of harmony search algorithm for constructing variable-strength t-way strategy. Method: Implemented in Java, HSS integrates the harmony search algorithm as parts of its search engine. Result: Benchmarking results demonstrate that HSS gives competitive results against most existing AI-based (and pure computational) counterparts. However, unlike other AI-based counterparts, HSS addresses the support for high interaction strength and permits the support for constraints. Conclusion: AI-based t-way strategies tend to outperform the pure computational-based strategies in terms of test size.
summary: @cite_30 developed a @math strategy based on the harmony search algorithm (HSS). The HSS mimics the behaviour of musicians trying to compose good music either by improvising on the best tune they remember or by random sampling. In doing so, the HSS iteratively exploits the harmonic memory to store the best solution found through a number of defined probabilistic improvisations within its local and global search processes. In each improvisation, one test case is selected for the final test suite until all the required interactions are covered. The notable feature of the HSS is that it supports constraints using the forbidden tuple approach.