• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (9): 159-164.doi: 10.3901/JME.2015.09.159

• 数字化设计与制造 • 上一篇    下一篇

基于多层编码的遗传-粒子群融合算法流水线优化控制

侯媛彬, 薛斐, 郑茂全, 樊荣   

  1. 西安科技大学电气与控制工程学院
  • 出版日期:2015-05-05 发布日期:2015-05-05

Optimization of Flow-shop Control by Using Genetic-particle Swarm Algorithm of Multilayer-coded

HOU Yuanbin, XUE Fei, ZHENG Maoquan, FAN Rong   

  1. College of Electrical and Control Engineering, Xi’an University of Science and Technology
  • Online:2015-05-05 Published:2015-05-05

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。

关键词: 多层编码, 流水线优化控制, 遗传-粒子群融合算法, 最短加工时间

Abstract: 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.

Key words: flow-shop optimizing control, genetic-particle swarm algorithm, minimize production time, multilayer-coded

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