局部搜索量子遗传算法及其无功优化应用  

Quantum-inspired Genetic Algorithm with Local Search and Its Applications in Reactive Power Optimization

作　　者：刘红文[1] 张葛祥[1] 

机构地区：[1]西南交通大学电气工程学院,成都610031

出　　处：《电力系统及其自动化学报》2009年第2期6-10,共5页Proceedings of the CSU-EPSA

基　　金：国家自然科学基金资助项目(60702026)

摘　　要：针对量子遗传算法局部寻优能力差的不足,提出一种局部搜索量子遗传算法,用于电力系统无功优化。该方法将局部搜索引入到量子遗传算法中,先进行全局寻优,当全局寻优搜索到的最优解经过多次迭代没有变化时,在此解附近产生小的寻优区间,进行局部寻优,以使算法同时具有较强的全局和局部搜索能力。复杂测试函数和IEEE 30节点测试系统的仿真实验表明,该方法在寻优能力、收敛速度和稳定性方面优于文献中的新量子遗传算法、进化规划等多种方法。For the local search capability of quantum-inspired genetic algorithm （QGA） is Limited, a quantum-inspired genetic algorithm with local search （LSQGA） is presented to solve the reactive power optimization problem. This technique introduces local search into QGA for searching global solution. In the process of searching global solution, if the searched best solution is not improved in certain successive iterations, local search is applied to explore the neighbor domain of the solution. LSQGA has better global and local search capabilities simultaneously. Experiments are carried out on complex functions and IEEE30- bus system, and show that LSQGA is competitive to several other optimization methods such as novel quantum genetic algorithm and evolution strategy, in terms of search capability, convergence speed and stability.

关 键 词：电力系统 无功优化 量子遗传算法 局部搜索量子遗传算法 

分 类 号：TM734[电气工程—电力系统及自动化]