东南大学学报（自然科学版）Issue(1)：23-29,7.DOI:10.3969/j.issn.1001-0505.2016.01.005
带交叉算子的量子粒子群优化算法
Quantum particle swarm optimization algorithm with crossover operator
摘要
Abstract
In order to improve the performance of the quantum particle swarm optimization ( QPSO) algorithm and its ability to solve multimodal optimization problems, by using a new calculation method for the point of interest and the characteristic length of the potential well, an improved QPSO algorithm with crossover operator, named as CQPSO algorithm, is proposed by introducing the crossover operator in the genetic algorithm and incorporating the adaptive parameter control technolo-gy of crossover probability.The CQPSO algorithm can not only ensure the diversity of the particle group and the vigor of the particles, but also overcome the instability of convergence and accidental fall into local optimum in some special scenarios.The experimental results show that in 21 standard test functions, on the same physical simulation platform, as for whether unimodal functions, multi-modal functions, offset or rotating functions, the CQPSO algorithm is superior to other improved QPSO algorithms in performance in most cases, and its effectiveness and robustness are proved.关键词
量子粒子群优化/交叉算子/局部优化/多峰函数/收敛Key words
quantum particle swarm optimization/crossover operator/local optimization/multimo-dal function/convergence分类
信息技术与安全科学引用本文复制引用
陈汉武,朱建锋,阮越,刘志昊,赵生妹..带交叉算子的量子粒子群优化算法[J].东南大学学报（自然科学版）,2016,(1):23-29,7.基金项目
国家自然科学基金资助项目（61170321，61502101）、高等学校博士学科点专项科研基金资助项目（20110092110024）、江苏省自然科学基金资助项目（BK20140651）． （）
