A novel software clustering approach-based on large-scale many-objective optimization for object-oriented systems

Large-scale many-objective optimization problems (LSMaOPs) present a significant challenge for conventional evolutionary algorithms when trying to find optimal solutions. To overcome the limitations of conventional evolutionary algorithms in handling LSMaOPs, various improved algorithms, known as large-scale many-objective optimization algorithms (LSMaOAs), have been introduced. However, real-world LSMaOPs, particularly Large-Scale Many-Objective Software Clustering Problems (LSMaSCPs), demand more effective optimization approach. This paper introduces an Information Feedback Model (IFM) based LSMaOA called Large-scale Many-Objective Harmony Search (LSMaHS) designed to tackle the LSMaSCPs. To evaluate the performance of different variants of LSMaHS, they are tested on five different LSMaSCP instances and compared with existing LSMaOAs. The experimental results demonstrate that the variants LSMaHS-F1 and LSMaHS-R1 significantly outperform existing approaches, especially on more than 12 and 8 test problems out of 20 test problems, respectively. Thus, the proposed approach can assist software engineers more effectively in improving the maintainability and modularity of large applications.