摘要
针对传统粒子群优化算法生成测试数据容易产生早熟收敛而陷入局部最优的问题,提出一种基于改进粒子群算法的组合测试数据生成算法。该算法在粒子群算法的基础上引入一种惯性权重自适应调整策略,根据粒子的适应度不同采用不同的惯性权重,从而有效的平衡算法的全局和局部搜索能力,增加种群的多样性并提高算法的搜索效率。仿真实验表明该算法与传统粒子群算法相比,所需迭代次数减少,生成组合测试数据速度快。
To solve the problem of the traditional Particle Swarm Optimization （PSO） algorithm＇s premature convergence and local optimum, an improved PSO is presented for test data generation in combinatorial testing. Based on the traditional PSO, inertial weight adaptive adjust- ment strategy has been used. In the new algorithm, particles have different inertia weight with different fitness. These strategies improve the PSO algorithm at the aspects of diversity and the balance of exploration and exploitation. Simulation results show that the improved algorithm obviously reduces the number of iterations, improves the speed of combinatorial test data generation.
出处
《西安邮电学院学报》
2012年第3期48-52,共5页
Journal of Xi'an Institute of Posts and Telecommunications
基金
2010国家自然科学基金资助项目(61050003)
关键词
组合测试
粒子群优化算法
测试数据
惯性权重
combinatorial testing, particle swarm optimization algorithm （PSO）, test data, inertia weight
