  Although showing competitive performances in many real-world optimization
problems, Teaching Learning based Optimization Algorithm (TLBO) has been
criticized for having poor control on exploration and exploitation. Addressing
these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning
based Optimization (ATLBO) has been developed in the literature. This paper
describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based
Optimization (ATLBO) for software module clustering problem. Comparative
studies with the original Teaching Learning based Optimization (TLBO) and other
Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to
its adaptive selection of search operators based on the need of the current
search.
