一种基于Kalman滤波和粒子群优化的测试数据生成方法

为减少进化代数,提高路径覆盖成功率,提出了多邻域Kalman滤波PSO测试数据生成方法.在该方法中将粒子固定划分到不同邻域中,各邻域内指定一个粒子向全局最优粒子学习,其余各粒子向所在邻域中最优粒子学习,而全局最优粒子利用无速度项的简化PSO进化.在此过程中,除全局最优粒子外的各粒子利用Kalman滤波方程更新粒子的位置.实验表明,相较于基本PSO和其他PSO方法,即使是覆盖困难的路径,本文方法也具有进化代数少、路径覆盖成功率高及性能稳定的特点.

A Test Data Generation Method Based on Kalman Filter and Particle Swarm Optimization Algorithm

A test data generation method named multi-neighborhood Kalman filter PSO (MNKFPSO) was proposed to reduce the evolution number and to improve the success rate of path coverage.Particles except the global best one update themselves' positions using Kalman filter.One of them is allotted to a fixed neighborhood.A designated particle learns from the global best particle,others learn from the best in one neighborhood.And the global best particle's position changes by a simple PSO which discards the particle velocity.The experimental results show that it can generate test data covering the specified path in the less evolutionary using MNKFPSO and has high success rate of path coverage even though the paths difficult to cover.The algorithm also exhibits a stable performance.