Software Module Clustering based on the Fuzzy Adaptive Teaching Learning based Optimization Algorithm
This work addresses software engineering challenges by improving module clustering, but it is incremental as it adapts an existing optimization method to a specific domain.
The paper tackled the software module clustering problem by applying a new variant of the Teaching Learning based Optimization algorithm, called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO), which demonstrated superior performance compared to the original TLBO and other fuzzy variants.
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.