[1]王莉荣,祁云嵩.基于函数最优解问题的粒子群算法改进[J].计算机技术与发展,2013,(02):49-51.
　WANG Li-rong,QI Yun-song.Partical Swarm Optimization Improvement Based on Optimal Solution to Functions[J].,2013,(02):49-51.
点击复制

基于函数最优解问题的粒子群算法改进()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年02期
页码:
49-51
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Partical Swarm Optimization Improvement Based on Optimal Solution to Functions
文章编号:
1673-629X（2013）02-0049-03
作者:
王莉荣; 祁云嵩
江苏科技大学 计算机科学与工程学院
Author(s):
WANG Li-rong; QI Yun-song
关键词:
粒子群算法; 压缩因子; 时变权重
Keywords:
partical swarm optimization algorithm; conpression factor; time-varying weights
文献标志码:
A
摘要:
通过对粒子群算法的深入研究,鉴于其具有容易陷入局部极值、迭代后期收敛速度慢、精度低等情况,众多学者对其作出改进,并都已成功应用到各种实际问题中.为了改善粒子群算法性能,能够快速准确地求解出函数的最优解,文中在基于粒子群最优算法及其改进算法研究的基础上,结合时变权重与压缩因子,对粒子群算法进行改进,并将改进算法应用于求解函数最优解问题中.实验表明,该方法具有了带时变权重或带压缩因子算法的优点,同时加快了函数的收敛速度,提高了最优解的准确度,通过参数调整,性能得到了有效改善
Abstract:
A depth-research found that partical swarm algorithm is easy to fall into local minima,iterative post-slow rate of convergence, low accuracy and so on,then many scholars have made improvements and successfully applied to a variety of practical problems. In order to improve the performance of partical swarm optimization,it works over the particle swarm optimization and the proved methods in order to solve the function of the optimal solution fastly and accurately. And it combines the time-varying weights with the constriction factor to improve the partical swarm optimization. Then use the method to solve the optimal solution of functions. Experiments show that the method has the advantages with the band time-varying weights or with a compression factor algorithm,at the same time makes the con-vergence faster,improves the accuracy of the optimal solution of the function,and improve the performance by adjusting parameters

相似文献/References:

[1]张爱华 江中勤 张华.基于粒子群优化算法的分形图像压缩编码[J].计算机技术与发展,2010,(02):21.
　ZHANG Ai-hua,JIANG Zhong-qin,ZHANG Hua.Fractal Image Compression Coding Based on PSO[J].,2010,(02):21.
[2]唐俊.PSO算法原理及应用[J].计算机技术与发展,2010,(02):213.
　TANG Jun.Principle and Application of PSO Algorithm[J].,2010,(02):213.
[3]张捍东 廖天红 岑豫皖.用模拟退火思想的粒子群算法实现图像分割[J].计算机技术与发展,2010,(05):83.
　ZHANG Han-dong,LIAO Tian-hong,CEN Yu-wan.Image Segmentation Through Particle Swarm Optimization Based on Simulated Annealing[J].,2010,(02):83.
[4]廖锋 高兴宝.差分演化算法在约束优化问题中的应用[J].计算机技术与发展,2010,(05):187.
　LIAO Feng,GAO Xing-bao.Application of Differential Evolution Algorithms on Constraint Optimization Problems[J].,2010,(02):187.
[5]来磊 卢文科 邓开连.基于二维Tsallis交叉熵直线型图像阈值分割方法[J].计算机技术与发展,2010,(06):105.
　LAI Lei,LU Wen-ke,DENG Kai-lian.New Image Thresholding Segmentation Methods Based on Two-Dimensional Tsallis Cross-Entropy Liner-Type[J].,2010,(02):105.
[6]邹毅 朱晓萍 王秀平.一种基于混沌优化的混合粒子群算法[J].计算机技术与发展,2009,(11):18.
　ZOU Yi,ZHU Xiao-ping,WANG Xiu-ping.A Hybrid PSO Algorithm Based on Chaos Optimization[J].,2009,(02):18.
[7]王为为 程家兴 贺晟.基于佳点集交叉的粒子群算法[J].计算机技术与发展,2009,(12):32.
　WANG Wei-wei,CHENG Jia-xing,HE Sheng.Particle Swarm Algorithm Based on Good Point Set Crossover[J].,2009,(02):32.
[8]贾瑞玉 黄义堂 邢猛.一种动态改变权值的简化粒子群算法[J].计算机技术与发展,2009,(02):137.
　JIA Rui-yu,HUANG Yi-tang,XING Meng.A Modified Simple Particle Swarm Optimization Using Dynamically Decreasing Inertia Weight[J].,2009,(02):137.
[9]卢珊萍 于盛林.基于粒子群算法的细胞神经网络模板参数设计[J].计算机技术与发展,2009,(04):83.
　LU Shan-ping,YU Sheng-lin.A Template Design Method for Cellular Neural Network Based on Particle Swarm Optimizer Algorithm[J].,2009,(02):83.
[10]王艳玲 李龙澍 胡哲.群体智能优化算法[J].计算机技术与发展,2008,(08):114.
　WANG Yan-ling,LI Long-shu,HU Zhe.Swarm Intelligence Optimization Algorithm[J].,2008,(02):114.

更新日期/Last Update: 1900-01-01