Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

摘要: The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world problems. Nevertheless, this requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped local optima), as well enhance solution diversity. Thus, paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, permit adaptive selection of its global search operations. In order assess performances, we adopt ATLBO the mixed strength t-way test generation problem. Experimental results reveal that exhibits performances against original other meta-heuristic counterparts.