摘要
量子粒子群优化算法(QPSO)避免了粒子群算法(PSO)不能保证收敛到全局最优解的缺点,认为粒子具有量子的行为,并且可以在整个可行解空间进行搜索。无功优化问题是带有离散变量的非线性、不连续、多约束、多变量的复杂优化问题,应用QPSO算法并结合动态调整罚函数的方法来解决无功优化问题。通过对IEEE-30节点和IEEE-14节点系统进行仿真计算,并与PSO算法、GA算法进行比较,表明该算法能更好地获得全局最优解。
Unlike Particle swarm optimization,quantum-behaved particle swarm optimization（QPSO）can assure the convergence of global optimality,because particles have quantum-behave,and is able to carry out feasible solution search.Reactive power optimal has a series complicated problems such as discrete variables of linear,discontinuous,multiple constraints,multiple variables,QPSO combined with dynamic adjusting of penalty function is used to optimize reactive power.By simulation of IEEE-30 node and IEEE-14 node,comparing them with PSO and GA algorithm,the result shows QPSO can do better in global optimal solution.
出处
《东北电力技术》
2011年第5期1-4,共4页
Northeast Electric Power Technology
关键词
量子粒子群算法
全局最优
无功优化
动态罚函数
Quantum-behaved particle swarm optimization
Global optimal
Reactive power
Dynamic adjusting of penalty function
