| 基于混合微粒群优化的多目标柔性Job-shop调度 |
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| 引用本文： | 夏蔚军,吴智铭. 基于混合微粒群优化的多目标柔性Job-shop调度[J]. 控制与决策, 2005, 20(2): 137-141 |
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| 作者姓名： | 夏蔚军 吴智铭 |
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| 作者单位： | 上海交通大学,自动化系,上海,200030;上海交通大学,自动化系,上海,200030 |
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| 基金项目： | 国家自然科学基金项目(70071017). |
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| 摘 要： | 应用传统方法求解多目标柔性Job-shop调度问题是十分困难的,微粒群优化采用基于种群的搜索方式,融合了局部搜索和全局搜索,具有很高的搜索效率.模拟退火算法使用概率来避免陷入局部最优,整个搜索过程可由冷却表来控制.通过对这两种算法的合理组合,建立了一种快速且易于实现的新的混合优化算法.实例计算以及与其他算法的比较说明,该算法是求解多目标柔性Job-shop调度问题的可行且高效的方法.
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| 关 键 词： | 多目标 柔性Job-shop调度 微粒群优化 模拟退火 混合优化算法 |
| 文章编号： | 1001-0920(2005)02-0137-05 |
| 修稿时间： | 2004-04-12 |
Hybrid particle swarm optimization approach for multi-objective flexible job-shop scheduling problems |
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| XIA Wei-jun,WU Zhi-ming. Hybrid particle swarm optimization approach for multi-objective flexible job-shop scheduling problems[J]. Control and Decision, 2005, 20(2): 137-141 |
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| Authors: | XIA Wei-jun WU Zhi-ming |
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| Abstract: | Particle swarm optimization (PSO) is discussed, which combines local search and global search, (possessing) high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum. By reasonably hybridizing these two methodologies, an easily (implemented) (hybrid) algorithm for the multi-objective (Flexible) job-shop scheduling problem (FJSP) is presented. The computational results show that the proposed algorithm is a viable and effective approach for the multi-objective FJSP. |
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| Keywords: | multi-objective flexible job-shop scheduling particle swarm optimization simulated annealing hybrid optimization algorithm |
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