基于遗传-粒子群混合算法的测试数据自动生成

Automatic test data generation based on GA-PSO

针对传统遗传算法(GA)容易产生早熟收敛和易陷入局部最优解的问题,提出了一种基于遗传-粒子群混合算法(GA-PSO)的软件测试数据自动生成算法:以粒子群优化算法(PSO)为主线,按PSO算法中标准的速度和位置更新,将GA算法的筛选、交叉、变异与PSO算法的自动更新特征结合在一起,使所有测试数据在局部区域中再次寻找最优值,从而避免了过早收敛,改进了搜索最佳值的能力.仿真实验表明:遗传-粒子群混合算法具有更快的收敛速度,保持了种群的多样性,提高了全局搜索能力.

The traditional genetic algorithm（GA） easily engender prematures convergence and is easy to fall into local optimal solution of the problem.A hybrid algorithm of genetic algorithm and particle swarm optimization（GA-PSO）was presented for automatic generation of test data.Particle swarm optimization（PSO）serves as main line,according to the PSO algorithm＇s speed and location update,the GA algorithm selection,crossover,rutation combined the PSO algorithm automatic update features.All test data at the local region were finded the optimal value again in order to avoid premature convergence and it improve the ability to search the best value.Simulation results showed that the hybrid algorithm had a faster convergence rate,thus maintaining the population diversity and improving the global search capabilities.