摘要
针对机械加工行业中多任务加工的流水线自动优化控制问题,建立了以'多工件多工序最短加工时间'为目标,以'实际加工限制'为约束的流水线加工控制数学模型,提出一种基于多层编码的遗传-粒子群融合算法。该算法以粒子群算法思想为基础,融合了遗传算法操作和多层编码机制,可防止陷入局部最优解,并具有较快的收敛速度。针对不同规模的加工实例进行仿真,结果表明该算法完成多工件多工序的加工时间比基本遗传算法缩短了约9%,具有提高流水线利用率和机械加工生产效率的优点。
A genetic-particle swarm optimization algorithm of multilayer-coded is proposed to solve the flow shop’s optimizing control problem of machinery industry. The objective of the issue is to minimize the production time. Through analyzing the actual machining conditions, control mathematical model of production line is set up. The algorithm bases on particle swarm algorithm and combines the genetic swarm optimization with the multilayer-coded mechanism. The algorithm has a higher speed and is used to avoid being trapped into local minima. The results show that the multilayer-coded genetic-particle swarm optimization algorithm has saved 9%times from the basic genetic algorithm. It has advantages of improving utilization ratio and production efficiency in machining line.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2015年第9期159-164,共6页
Journal of Mechanical Engineering
关键词
流水线优化控制
最短加工时间
多层编码
遗传-粒子群融合算法
flow-shop optimizing control
minimize production time
multilayer-coded
genetic-particle swarm algorithm
