摘要
针对全连接拓扑结构的粒子群算法在生成测试数据过程中,存在收敛精度低,易陷入局部极值的问题,提出一种混合粒子群算法HPSO,并将其应用于测试数据自动生成。该算法在保证全局收敛性的前提下,对多样性匮乏的种群,首先采用定长环形拓扑结构取代粒子群的全连接拓扑结构;其次,采用轮盘赌方法选择候选解,更新粒子位置信息和速度信息;最后引入条件禁忌算法,对处于局部极值的粒子采取禁忌处理。通过实验比较表明:与基本粒子群算法(BPSO)相比,HPSO使种群多样性得到大幅度提升;在测试数据生成性能上,HPSO的搜索成功率和路径覆盖率均优于遗传算法与粒子群算法混合算法GA-PSO,而平均耗时与BPSO算法相当,性能表现优越。
Since the fully connected topology of particle swarm algorithm has low convergence precision and easily falls into local extremum, an approach for automatically generating structural test data based on a hybrid particle swarm algorithm named HPSO （ Hybrid Particle Swarm Optimization） was proposed. Firstly, under the premise of global convergence, the population which lacked of diversity used fixed-length ring topology to replace the fully connected one. Secondly, the roulette wheel method was introduced to select the candidate solutions and update the location information and velocity information. Lastly, for controlling and directing the particles to escape from local minimum, the conditions of tabu search algorithm were introduced too. The result of experiment shows that HPSO has a better performance than the Basic Particle Swarm Optimization （BPSO） in population diversity. And HPSO exhibited superiority in search success rate and path coverage in contract with combination method of Genetic Algorithm and Particle Swarm Optimization algorithm named GA-PSO in test data generation, while the average time-consuming is not much different from BPSO.
出处
《计算机应用》
CSCD
北大核心
2015年第2期545-549,共5页
journal of Computer Applications
关键词
测试数据生成
全连接粒子群
拓扑结构
轮盘赌选择法
条件禁忌算法
test data generation
Global Particle Swarm Optimization （GPSO）
topological structure
roulette selection method
conditional tabu search algorithm
