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

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• 作者：Kamal Z. Zamli^a ; ^{kamalz@ump.edu.my} ; Fakhrud Din^a ; ^{fakhruddin@uom.edu.pk} ; Salmi Baharom^b ; ^{salmi@upm.edu.my} ; Bestoun S. Ahmed^c ; ^{bestoon82@gmail.com}
• 关键词：Software testing ; t-way testing ; Teaching learning-based optimization algorithm ; Mamdani fuzzy inference system
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：59
• 期：Complete
• 页码：35-50
• 全文大小：2666 K
• 卷排序：59

文摘

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.