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。
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 ofβ-PSO algorithm with standard particle swarm optimization(PSO)and cosine inertia weight improved particle swarm optimization(CPSO),the processing time ofβ-PSO algorithm is lower.The experimental results verify the effectiveness of theβ-PSO algorithm in reducing the processing time of flexible jobs.
作者
曲鹏举
QU Pengju(Engineering Training Center,Guizhou Institute of Technology,Guiyang 550003,China)
出处
《机械与电子》
2023年第1期3-6,12,共5页
Machinery & Electronics
基金
贵州省教育厅青年科技人才成长项目(黔教合KY字[2018]243)。
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
particle swarm optimization
cosine power function adaptive weight
beta distribution
minimum processing time
