...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Production Research >A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks
【24h】

A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks

机译：具有多处理器任务的混合流水车间调度的粒子群优化算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The multistage hybrid flow-shop scheduling problem with multiprocessor tasks has been found in many practical situations. Due to the essential complexity of the problem, many researchers started to apply metaheuristics to solve the problem. In this paper, we address the problem by using particle swarm optimization (PSO), a novel metaheuristic inspired by the flocking behaviour of birds. The proposed PSO algorithm has several features, such as a new encoding scheme, an implementation of the best velocity equation and neighbourhood topology among several different variants, and an effective incorporation of local search. To verify the PSO algorithm, computational experiments are conducted to make a comparison with two existing genetic algorithms (GAs) and an ant colony system (ACS) algorithm based on the same benchmark problems. The results show that the proposed PSO algorithm outperforms all the existing algorithms for the considered problem.
机译：在许多实际情况中都发现了带有多处理器任务的多阶段混合流水车间调度问题。由于问题的本质复杂性，许多研究人员开始应用元启发式方法来解决问题。在本文中，我们通过使用粒子群优化（PSO）解决了这一问题，粒子群优化是一种受鸟类群聚行为启发的新型元启发式方法。提出的PSO算法具有多种功能，例如新的编码方案，最佳速度方程的实现以及几种不同变体之间的邻域拓扑，以及有效结合了局部搜索。为了验证PSO算法，进行了计算实验，以与基于相同基准问题的两个现有遗传算法（GA）和蚁群系统（ACS）算法进行比较。结果表明，针对所考虑的问题，所提出的PSO算法优于所有现有算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号