[1]崔长彩,张耕培,傅师伟,等.利用粒子群优化算法的平面度误差评定[J].华侨大学学报(自然科学版),2008,29(4):507-509.[doi:10.11830/ISSN.1000-5013.2008.04.0507]
　CUI Chang-cai,ZHANG Geng-pei,FU Shi-wei,et al.Particle Swarm Optimization-Based Flatness Evaluation[J].Journal of Huaqiao University(Natural Science),2008,29(4):507-509.[doi:10.11830/ISSN.1000-5013.2008.04.0507]
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利用粒子群优化算法的平面度误差评定()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第29卷
期数:
2008年第4期
页码:
507-509
栏目:
出版日期:
2008-10-20

文章信息/Info

Title:
Particle Swarm Optimization-Based Flatness Evaluation
文章编号:
1000-5013(2008)04-0507-03
作者:
崔长彩; 张耕培; 傅师伟; 黄富贵
华侨大学机电及自动化学院
Author(s):
CUI Chang-cai;  ZHANG Geng-pei;  FU Shi-wei;  HUANG Fu-gui
College of Mechanical Engineering and Automation, Huaqiao University, Quanzhou 362021, China
关键词:
粒子群优化算法;  平面度;  惯性权重;  编码策略;  评定;  收敛性
Keywords:
particle swarm optimization;  flatness;  inertia weight;  encoding strategy;  evaluation;  convergence
分类号:
TP18
DOI:
10.11830/ISSN.1000-5013.2008.04.0507
文献标志码:
A
摘要:
将粒子群优化算法(PSO)应用于平面度误差的优化计算中.应用实例表明,PSO能够很好地解决具有非线性优化目标函数或具有多参数的优化问题; PSO的计算精度优于最小二乘法的计算精度,与其他满足标准定义的最小区域条件的方法计算精度相当,能够获得精度较高的结果且简单易于实现.实例计算和理论分析证明算法是收敛的,其中理论分析条件:(1)惯性权重ω<1; (2)随机参数组合c>0; (3)2ω-c+2>0.
Abstract:
A novel method for flatness evalution based on particle swarm optimization(PSO) was proposed.The case study of computation shows that the PSO is very suitable for dealing with the optimization problems with nonlinear optimization goal function or with multi-parameters.The advantages are as following: Its computation results are superior to those given by the least squared methods and as good as those given by other declared optimization methods; also it is efficient,easy to be understood and performed,and convergent verified by the computation and the theoretical analysis under the conditions of(1) inertia ω<1,(2) random parameters combination c>0,and(3) 2ω-c+2>0.

参考文献/References:

[1] KENNEDY J, EBERHART R. Particle swarm optimization [A]. Piscataway, N.J:Institute of Electrical and Electronics Engineers, 1995.1942-1948.
[2] 崔长彩, 李兵, 张认成. 粒子群优化算法 [J]. 华侨大学学报(自然科学版), 2006(4):343-347.doi:10.3969/j.issn.1000-5013.2006.04.002.
[3] SHI Y, EBERHART R. A modified particle swarm optimizer [A]. Piscataway, N.J:Institute of Electrical and Electronics Engineers, 1998.69-73.
[4] WEBER T, MOTAVALLI S, FALLAHI B. A unified approach to form error evaluation [J]. Precision Engineering, 2002(3):269-278.
[5] CUI Chang-cai, LI Bing, HUANG Fu-gui. Genetic algorithm based form error evaluation [J]. Measurement Science and Technology, 2007(7):1818-1824.
[6] TRELEA I C. The particle swarm optimization algorithm:Convergence analysis and parameter selection [J]. Information Processing Letters, 2003(6):317-325.doi:10.1016/S0020-0190(02)00447-7.
[7] JIANG M, LOU Y P, YANG S Y. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm [J]. Information Processing Letters, 2007(1):8-16.doi:10.1016/j.ipl.2006.10.005.

备注/Memo

备注/Memo:
福建省自然科学基金资助项目(T0850004); 福建省科技计划重点项目(2008I0020)
更新日期/Last Update: 2014-03-23