The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.The teaching learning-based optimization algorithm (TLBO) [6,7] adopts a simplistic approach of disregarding the control parameters (i.e., parameter free). TLBO specifically performs both global and local search sequentially per iteration to balance exploration and exploitation. Given that exploration and exploitation are dynamic in nature depending on the current search space region, any preset division between the two can be counter-productive and may lead to poor quality solutions. This paper addresses these issues through a new TLBO variant, adaptive TLBO (ATLBO) integrated with the Mamdani-type fuzzy inference system [8,9]. ATLBO adaptively selects its local and global search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem.Our contributions are summarized as follows:The novel ATLBO strategy based on the Mamdani-type fuzzy inference system is presented for exploration (i.e., global search) and exploitation (i.e., local search) selection. ATLBO is the first TLBO-variant strategy that addresses generation for both uniform and mixedstrength t-way test suite.This study is organized as follows. Section 2 presents the theoretical framework that covers the generation problem of t-way test and its mathematical notation. Section 3 describes the related work. Section 4 highlights the original TLBO algorithm and its variants, along with its applications. Section 5 outlines the novel ATLBO. Benchmark experiments are presented in Section 6. Section 7 and 8 discusses the experimental observations and validity threats, respectively. Finally, Section 9 concludes this study and presents the scope for future work.
Covering Array (CA) and the Generation Problem of Mixed-Strength t-way TestThe generation problem of t-way test is often associated with CA notation, where t represents the desired interaction strength. A CA (N; t, p, v), which is also expressed as CA (N; t, v p ), is a combinatorial structure constructed as an array of N rows and p columns (i.e., parameters) on v values, such that every N × t sub-array contains all ordered subsets from the v values of size t at least once [10]. When the number of component values varies, this condition can be handled by a mixed CA (MCA) (N; t, p, (v 1 , v 2 , …v i )) or MCA (N;...
