[1]陈汉武,朱建锋,阮越,等.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),2016,46(1):23-29.[doi:10.3969/j.issn.1001-0505.2016.01.005]
 Chen Hanwu,Zhu Jianfeng,Ruan Yue,et al.Quantum particle swarm optimization algorithm with crossover operator[J].Journal of Southeast University (Natural Science Edition),2016,46(1):23-29.[doi:10.3969/j.issn.1001-0505.2016.01.005]
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带交叉算子的量子粒子群优化算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第1期
页码:
23-29
栏目:
计算机科学与工程
出版日期:
2016-01-20

文章信息/Info

Title:
Quantum particle swarm optimization algorithm with crossover operator
作者:
陈汉武1朱建锋1阮越12刘志昊1赵生妹3
1东南大学计算机科学与工程学院, 南京 210096; 2安徽工业大学计算机科学与技术学院, 马鞍山 243005; 3南京邮电大学通信与信息工程学院, 南京 210003
Author(s):
Chen Hanwu1 Zhu Jianfeng1 Ruan Yue12 Liu Zhihao1 Zhao Shengmei3
1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
2School of Computer Science and Technology, Anhui University of Technology, Maanshan 243005, China
3College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
关键词:
量子粒子群优化 交叉算子 局部优化 多峰函数 收敛
Keywords:
quantum particle swarm optimization crossover operator local optimization multimodal function convergence
分类号:
TP387
DOI:
10.3969/j.issn.1001-0505.2016.01.005
摘要:
为了改善量子粒子群优化(QPSO)算法、提高其求解多峰优化问题的能力,采用新的粒子吸引点和势阱特征长度计算方法,引入遗传算法中的交叉算子并融入交叉概率自适应的参数控制技术,设计了一种带交叉算子的量子粒子群优化(CQPSO)算法.CQPSO算法既可确保QPSO粒子群体的多样性、维护粒子整体的活力性,又能克服特殊情况下QPSO算法收敛的不稳定性和陷入局部最优的偶发性.实验结果表明,在21个标准测试函数中,无论对应单峰函数、多峰函数或是偏移、旋转函数,在相同的物理仿真平台上,CQPSO算法的性能在绝大多数情况下都优于其他改进的量子粒子群算法,从而验证了CQPSO算法的有效性和鲁棒性.
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 technology 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, multimodal 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.

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备注/Memo

备注/Memo:
收稿日期: 2015-06-11.
作者简介: 陈汉武(1955—),男,博士,教授,博士生导师,hw_chen@seu.edu.cn.
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).
引用本文: 陈汉武,朱建锋,阮越,等.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),2016,46(1):23-29. DOI:10.3969/j.issn.1001-0505.2016.01.005.
更新日期/Last Update: 2016-01-20