| 改进微粒群算法求解模糊交货期Flow-shop调度问题 |
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| 引用本文： | 沈兵虎,柳毅,潘瑞芳.改进微粒群算法求解模糊交货期Flow-shop调度问题[J].计算机工程与应用,2006,42(34):36-38,72. |
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| 作者姓名： | 沈兵虎 柳毅 潘瑞芳 |
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| 作者单位： | 1. 浙江传媒学院,杭州,310018
2. 浙江传媒学院,杭州,310018;上海理工大学,管理学院,上海,200093 |
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| 基金项目： | 上海市教委资助项目;上海市重点学科建设项目 |
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| 摘 要： | 针对模糊交货期Flow-shop调度问题的特点,论文提出用微粒群这种具有快速收敛、全局性能好的迭代优化算法进行求解,并使用惩罚函数、增加数据记忆库和自适应变异机制等方法对微粒群算法进行改进,减少了算法陷入局部极值的可能性。通过仿真实例,改进微粒群算法的全局寻优、收敛性和克服早熟的能力均优于遗传、启发式算法。
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| 关 键 词： | 流水车间调度 模糊交货期 微粒群算法 遗传算法 惩罚函数 |
| 文章编号： | 1002-8331（2006）34-0036-03 |
| 收稿时间： | 2006-09 |
| 修稿时间： | 2006-09 |
Modified PSO Algorithm Solving Flow-shop Scheduling Problem with Fuzzy Delivery Time |
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| SHEN Bing-hu,LIU Yi,PAN Rui-fang.Modified PSO Algorithm Solving Flow-shop Scheduling Problem with Fuzzy Delivery Time[J].Computer Engineering and Applications,2006,42(34):36-38,72. |
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| Authors: | SHEN Bing-hu LIU Yi PAN Rui-fang |
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| Abstract: | According to the flow shop scheduling problem with fuzzy delivery time,this paper uses particle swarm optimization(PSO) to solve this problem,which has good convergence speed and performance in searching global optimum. The modified PSO algorithm can immune to the local extremum by using some methods of penalty function,memory database,self-adaptive mutation and so on.The results of simulation indicate that modified PSO algorithm perform better than the genetic algorithm and the heuristic algorithm on the searching speed of global optimum,convergence speed and avoiding prematurity. |
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| Keywords: | flow shop scheduling problem fuzzy due date particle swarm optimization genetic algorithm penalty function |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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