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

TL;DR

This paper introduces ATLBO, a fuzzy adaptive variant of TLBO, designed to improve solution diversity and prevent premature convergence in generating mixed strength t-way test suites, demonstrating competitive performance.

Contribution

The paper proposes a novel fuzzy inference-based adaptive TLBO variant (ATLBO) for test suite generation, enhancing exploration and exploitation balance.

Findings

ATLBO outperforms original TLBO in test suite quality.

ATLBO shows competitive results against other meta-heuristics.

Fuzzy adaptation improves convergence and diversity.

Abstract

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.