Software Module Clustering based on the Fuzzy Adaptive Teaching Learning based Optimization Algorithm

TL;DR

This paper introduces an adaptive fuzzy variant of the Teaching Learning based Optimization algorithm to improve software module clustering, demonstrating superior performance over existing TLBO methods.

Contribution

The paper proposes a novel Fuzzy Adaptive TLBO algorithm specifically designed for software module clustering, enhancing exploration and exploitation capabilities.

Findings

ATLBO outperforms original TLBO in clustering tasks

Adaptive operator selection improves search efficiency

Experimental results show superior clustering quality

Abstract

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.