中国电机工程学报2012，Vol.32Issue(18)：108-115,8.
多样性引导的改进量子粒子群优化算法及其在干式空心电抗器优化设计中的应用
A Diversity-guided Modified QPSO Algorithm and Its Application in the Optimization Design of Dry-type Air-core Reactors
摘要
Abstract
According to the premature convergence of the quantum-behaved particle swarm optimization（QPSO） algorithm in solving complex problems,a diversity-guided modified QPSO（DGMQPSO） algorithm is proposed.The algorithm extends the mixed-probability-distribution based QPSO algorithm by using the populations＇ diversity information to guide the search of the particles.The chaotic mutation is implemented to the position of the global optimal particle when the populations＇ diversity is less than the lower limit,so that the diversity of the populations is improved and the ability of jumping out of the local optimal solution is enhanced.Moreover,the optimization design results are analyzed by using the mutation of the different chaotic random sequence.The simulation results of a 50 kvar dry-type air-core reactor show that the DGMQPSO algorithm is of strong global search ability,good stability and excellent optimization performance.关键词
干式空心电抗器/优化设计/量子粒子群优化算法/多样性/混沌变异Key words
dry-type air-core reactor/optimization design/quantum-behaved particle swarm optimization(QPSO) algorithm/diversity/chaotic mutation分类
信息技术与安全科学引用本文复制引用
张成芬,赵彦珍,邹建龙,马西奎..多样性引导的改进量子粒子群优化算法及其在干式空心电抗器优化设计中的应用[J].中国电机工程学报,2012,32(18):108-115,8.基金项目
电力设备电气绝缘国家重点实验室中青年基础创新基金 （）
