A multi-agent evolutionary algorithm for software module clustering problems

Jinhuang Huang, Jing Liu, Xin Yao

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


The aim of software module clustering problems (SMCPs) is to automatically find a good quality clustering of software modules based on relationships among modules. In this paper, we propose a multi-agent evolutionary algorithm to solve this problem, labeled as MAEA-SMCPs. With the intrinsic properties of SMCPs in mind, three evolutionary operators are designed for agents to realize the purpose of competition, cooperation, and self-learning. In the experiments, practical problems are used to validate the performance of MAEA-SMCPs. The results show that MAEA-SMCPs can find clusters with high quality and small deviations. The comparison results also show that MAEA-SMCPs outperforms two existing multi-objective algorithms, namely MCA and ECA, and two existing single-objective algorithms, namely GGA and GNE, in terms of MQ.
Original languageEnglish
Number of pages14
JournalSoft Computing
Early online date4 Feb 2016
Publication statusE-pub ahead of print - 4 Feb 2016


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