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A multi-agent evolutionary algorithm for software module clustering problems
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DOI：10.1007/s00500-015-2018-5.png)
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Abstract
En 中文
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.
Keywords:
Software module clustering
Multi-agent evolutionary algorithm
Modularization quality
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