基于模糊自适应教学的混合强度t路测试集生成优化策略
Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites
摘要：
基于教学的优化（TLBO）算法在解决许多现实世界的优化问题中表现出了竞争力的性能。然而，该算法需要更好地控制开发和探索以防止过早收敛（即，陷入局部最优），以及增强解决方案的多样性。因此，本文提出了一种新的TLBO变体的基础上Mamdani模糊推理系统，称为ATLBO，允许自适应选择其全局和局部搜索操作。为了评估其性能，我们采用ATLBO混合强度的t-路测试生成问题。实验结果表明，ATLBO具有竞争力的性能对原来的TLBO和其他元启发式同行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
