抑制孤立簇的软件模块化优化算法  

Software modularization optimization algorithm with eliminating isolated clusters

作　　者：牟立峰[1] 王方媛 

机构地区：[1]上海大学悉尼工商学院,上海201899

出　　处：《计算机应用》2018年第3期791-798,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(71302051)~~

摘　　要：针对传统软件模块化指标在解决软件模块化问题时容易导致孤立簇的问题,提出改进型软件模块化指标IMQ作为进化算法的适应函数以有效抑制孤立簇现象,并以IMQ最大化为目标建立软件模块化的数学规划模型,设计符合问题特点的基于相似度竞争和选择机制的改进遗传算法(IGA)求解该模型。首先,运用边收缩方法的启发式策略生成高质量的初始解,并将其作为种子植入到初始种群中;然后,利用IGA对模型进行求解,在提升搜索效率的同时进一步提高解的质量;最后,运用真实数据和仿真数据进行对比实验。实验结果表明IMQ指标能有效减少孤立簇的数目,而IGA比传统的多点爬山算法(IHC)和基于分组编码(GNE)的遗传算法具有更强的寻优能力和鲁棒性。Considering the isolated cluster problem caused by traditional software modularization methods, a new metric named Improved Modularization Quality （IMQ） was proposed and used as the fitness function of an evolutionary algorithm to eliminate isolated clusters effectively. A mathematical programming model with the goal of maximizing IMQ was developed to represent software modularization problem. In addition, an Improved Genetic Algorithm （IGA） with competition and selection mechanism similarity was designed to solve this model. Firstly, a heuristic strategy based on edge contraction was used to generate high-quality solutions. Then the solutions were implanted as seeds into the initial population. At last, the proposed IGA was employed to further improve solution quality. Comparison experimental results prove that IMQ can effectively reduce the number of isolated clusters, and IGA has stronger robustness and ability of finding better solutions than Improved Hill Climbing Algorithm （IHC） and GA based on Group Number Encoding （GNE）.

关 键 词：软件模块化 软件聚类问题 遗传算法 启发式策略 产品设计 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]