混合拓扑结构的粒子群算法及其在测试数据生成中的应用研究  

MPSO and Its Application in Test Data Automatic Generation

作　　者：焦重阳 周清雷[1] 张文宁[2,3] JIAO Chong-yang;ZHOU Qing-lei;ZHANG Wen-ning(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;The PLA Information Engineering University,Zhengzhou 450001,China;Zhongyuan University of Technology,Zhengzhou 450001,China)

机构地区：[1]郑州大学信息工程学院,郑州450001 [2]中国人民解放军信息工程大学,郑州450001 [3]中原工学院,郑州450001

出　　处：《计算机科学》2017年第12期249-254,共6页Computer Science

基　　金：国家自然科学基金项目(61250007);河南省科技厅基础与前沿技术研究项目(152300410055)资助

摘　　要：粒子群算法(PSO)的拓扑结构是影响算法性能的关键因素,为了从根源上避免粒子群算法易陷入局部极值及早熟收敛等问题,提出一种混合拓扑结构的粒子群优化算法(MPSO)并将其应用于软件结构测试数据的自动生成中。通过不同邻域拓扑结构对算法性能影响的分析,采用一种全局寻优和局部寻优相结合的混合粒子群优化算法。通过观察粒子群的多样性反馈信息,对每一代种群粒子以进化时选择全局拓扑结构模型(GPSO)或局部拓扑结构模型(LPSO)的方法进行。实验结果表明,MPSO使得种群的多样性得到保证,避免了粒子群陷入局部极值,提高了算法的收敛速度。To date,meta-heuristic search algorithms have been applied to automate test data generation.The topology of particle swarm optimization(PSO)is one of the key factors that affect algorithm performance.In order to overcome the phenomena of falling into local optimal and premature convergence of the standard particle swarm algorithm(PSO),a mixture neighborhood structure(MPSO)was proposed to generate software structure test data automatically.Based on the analysis of the different neighborhood topology structure effect on the performance of particle optimization,this paper presented a new particle swarm optimization with mix topological structure.MPSO is based on the combination of global optimization and local optimization.In each generation,by observing the feedback information of diversity of the population,the particle speed update method is selected by global topology model or local topology model.The experimental results show that MPSO increases the swarm diversity,avoids falling into local optimization,and improves the convergence speed of the proposed algorithm.

关 键 词：粒子群算法 测试数据自动生成 拓扑结构 全局寻优 局部寻优 多样性 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]