摘要
将量子行为粒子群算法用于复杂电子设备测试点选取问题中。该算法以最少的测试点、测试代价和最大的故障隔离率、检测率为目标定义粒子适应度函数,保证了算法的全局最优性能。仿真结果表明,与其他算法相比,量子行为粒子群算法提高了测试点选取的效率,能较好的保证其算法全局最优性能,为粒子群算法的改进和多目标优化问题提供了新的思路。
A Quantum-Behaved Particle Swarm Optimization（QPSO） algorithm is applied to solve the test point selection of electronic system.The fitness function is defined based on the minimum test point and cost as well as maximum fault detection rate and fault isolation rate.Simulation shows that QPSO improves efficiency of test global optimization characteristic.It also provides a new way to improve particle swarm optimization algorithm and solve multi-objective optimization problem.
出处
《舰船电子工程》
2013年第3期105-107,共3页
Ship Electronic Engineering
关键词
量子行为粒子群算法
多目标优化
测试点选择
quantum-behaved particle swarm optimization
multi-objective optimization
test points selection
