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

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。

MPSO and Its Application in Test Data Automatic Generation

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.