[1]曲鹏举.改进粒子群算法在柔性作业加工时间问题研究[J].机械与电子,2023,41(01):3-6.
　QU Pengju.Research on Processing Time Problem of Improved Particle Swarm Optimization in Flexible Job[J].Machinery & Electronics,2023,41(01):3-6.
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改进粒子群算法在柔性作业加工时间问题研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
41
期数:
2023年01期
页码:
3-6
栏目:
设计与研究
出版日期:
2023-01-25

文章信息/Info

Title:
Research on Processing Time Problem of Improved Particle Swarm Optimization in Flexible Job
文章编号:
1001-2257 ( 2023 ) 01-0003-04
作者:
曲鹏举
贵州理工学院工程训练中心,贵州 贵阳 550003
Author(s):
QU Pengju
( Engineering Training Center , Guizhou Institute of Technology , Guiyang 550003 , China )
关键词:
粒子群算法; 幂函数自适应权重; 贝塔分布; 最小加工时间
Keywords:
particle swarm optimization ;  cosine power function adaptive weight ;  beta distribution ;  minimum processing time
分类号:
TP301 ; TH16
文献标志码:
A
摘要:
为了减少柔性作业加工时长,在柔性作业加工问题中,提出一种改进粒子群算法 ( β-PSO )。该算法以最小加工时间为目标函数,惯性权重幂函数自适应调节,随机数采用贝塔分布进行改进,选取 Kacem 算例进行验证,通过对比 β-PSO 算法与标准粒子群算法( PSO )、余弦惯性权重改进粒子群算法( CPSO )的优化结果,β-PSO 算法加工时间均较低。实验结果表明,β-PSO 算法在减少柔性作业加工时间问题上的有效性。
Abstract:
In order to reduce the processing time of the flexible job , an improved particle swarm algorithm( β-PSO ) is proposed in the study of the flexible job processing problem , the minimum processing time is designed as the algorithm objective function , the inertia weight power function is adaptively adjusted , the random number is improved by beta distribution.The Kacem example is selected for verification. By comparing the optimization results of β-PSO algorithm with standard particle swarm optimization( PSO ) and cosine inertia weight improved particle swarm optimization( CPSO ), the processing time of β-PSO algorithm is lower.The experimental results verify the effectiveness of the β-PSO algorithm in reducing the processing time of flexible jobs.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2022-08-02
基金项目:贵州省教育厅青年科技人才成长项目(黔教合 KY 字[ 2018 ] 243 )
作者简介:曲鹏举 ( 1988- ),男,河南林州人,硕士,讲师,研究方向为先进制造模式与制造信息系统。
更新日期/Last Update: 2023-02-28