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| Pareto最大最小蚂蚁算法及其在热轧批量计划优化中的应用 | |
| 引用本文： | 贾树晋,朱俊,杜斌,岳恒.Pareto最大最小蚂蚁算法及其在热轧批量计划优化中的应用[J].控制理论与应用,2012,29(2):137-144. |
| 作者姓名： | 贾树晋 朱俊 杜斌 岳恒 |
| 作者单位： | 1. 上海交通大学自动化系,系统控制与信息处理教育部重点实验室,上海200240 2. 东北大学信息科学与工程学院,辽宁沈阳110004／宝钢研究院自动化所,上海201900 3. 上海交通大学自动化系,系统控制与信息处理教育部重点实验室,上海200240／宝钢研究院自动化所,上海201900 4. 东北大学自动化研究中心,辽宁沈阳,110004 |
| 基金项目： | 国家重点基础研究发展计划资助项目(2009CB320604). |
| 摘 要： | 针对双目标旅行商问题提出了基于Pareto概念的最大最小蚂蚁算法(P--MMAS). 通过重新设计状态转移策略、信息素更新策略及局部搜索策略, 同时引入基于自适应网格的多样性保持策略与信息素平滑机制, 使算法能够快速搜索到在目标空间上均匀分布的近似Pareto前端. 通过在6个标准测试函数上的实验及在热轧批量计划优化中的应用, 表明P--MMAS具有良好的优化性能及实用性. |
| 关 键 词： | 蚁群算法 双目标旅行商问题 多目标优化 组合优化 热轧批量计划 |
| 收稿时间： | 2011/2/23 0:00:00 |
| 修稿时间： | 2011/6/15 0:00:00 |
Pareto max-min ant system algorithm and its application to hot rolling batch planning problem |
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| JIA Shu-jin,ZHU Jun,DU Bin and YUE Heng.Pareto max-min ant system algorithm and its application to hot rolling batch planning problem[J].Control Theory & Applications,2012,29(2):137-144. | |
| Authors: | JIA Shu-jin ZHU Jun DU Bin and YUE Heng |
| Affiliation: | Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education,School of Information Science & Engineering, Northeastern University; Research Institute of Automation, Academy of BaoSteel,Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education;Research Institute of Automation, Academy of BaoSteel,Research Center of Automation, Northeastern University |
| Abstract: | A Pareto concept-based max-min ant system algorithm for the bi-objective traveling salesman problem is proposed. By modifying the state transition rule, pheromone updating rule and the local search rule; as well as employing an adaptive-grid based diversity maintenance approach and the pheromone trail smoothing mechanism, we find the approximate Pareto front which is uniformly distributed on the objective space. Simulation on 6 benchmark functions and application to a hot rolling batch planning problem indicate that the proposed algorithm has desirable performance and practicability. |
| Keywords: | ant colony algorithm bi-objective traveling salesman problem multi-objective optimization combinatorial optimization hot rolling batch planning |
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