[1]徐玉杰,仇雷,刘清.自适应惯性权重的混沌粒子群算法研究[J].南京师范大学学报(工程技术版),2012,12(01):064-69.
　Xu Yujie,Qiu Lei,Liu Qing.Chaotic Particle Swarm Optimization With Adaptive Inertia Weight[J].Journal of Nanjing Normal University(Engineering and Technology),2012,12(01):064-69.
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自适应惯性权重的混沌粒子群算法研究
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
12卷
期数:
2012年01期
页码:
064-69
栏目:
出版日期:
2012-03-20

文章信息/Info

Title:
Chaotic Particle Swarm Optimization With Adaptive Inertia Weight
作者:
徐玉杰1; 仇雷2; 刘清1
( 1． 南京师范大学计算机科学与技术学院，江苏南京210046) ( 2． 解放军八二医院信息科，江苏淮安223001)
Author(s):
Xu Yujie1; Qiu Lei2; Liu Qing1
1.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210046,China
关键词:
PSO 算法; 早熟收敛; 混沌种群; 自适应惯性权重
Keywords:
PSO algorithms; premature convergence; chaos population; adaptive inertia weight
分类号:
TP18
摘要:
为了克服传统粒子群算法(PSO)的早熟和局部最优问题,提出了一种新的自适应惯性权重的混沌粒子群算法(ACP-SO算法).该算法采用分段Logistic混沌映射的方法产生初始种群,并根据种群的进化状态来动态调整惯性权重.在详细阐述算法的种群初始化过程和动态调整惯性权重的过程之后,对经典的测试函数分别采用几种改进的PSO算法和ACPSO算法对其进行了测试,与其他几种方法相比,ACPSO算法的全局搜索能力有了显著的提高,并且能有效地避免早熟收敛问题,同时也说明ACPSO算法应用的可行性和有效性.
Abstract:
To overcome the problem of premature convergence and local optimal in conventional particle swarm optimization ( PSO) ，a new adaptive inertia weight chaos particle swarm optimization ( ACPSO) is presented． The algorithm generates initial population with segmented logistic map，and varies inertia weight dynamically based on the evolutionary state of the population． After the detailed illustrations of how to generate initial population and how to adjust the inertia weight，this paper tests some classical functions with some improved PSO algorithms and ACPSO algorithm． Compared with other algorithms，the ACPSO algorithm not only has a great advantage of convergence property，but also avoids the premature convergence problem effectively，and at the same，it shows the feasibility and validity of the ACPSO algorithm．

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 61103185) 、江苏省高校自然科学基金( 10KJD520004) ．
通讯联系人: 刘清，博士，教授，研究方向: 智能测控技术． E-mail: njnulq@163． com
更新日期/Last Update: 2013-03-11