| 基于蚁群算法求解TSP问题的改进 |
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| 引用本文： | 陈洁,刘希玉,李庆波.基于蚁群算法求解TSP问题的改进[J].山东科学,2010,23(2):58-61. |
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| 作者姓名： | 陈洁 刘希玉 李庆波 |
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| 作者单位： | 1. 山东师范大学管理与经济学院，山东 济南 250014；2. 山东天辉科技有限公司通讯事业部，山东 济南 250100 |
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| 基金项目： | 国家自然科学基金重大项目，山东省自然科学基金重大项目，山东省"泰山学者"建设工程专项，山东省教育厅计划项目 |
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| 摘 要： | 蚁群算法虽然具有鲁棒性和发现较好解的能力,但其搜索时间较长,当规模较大时易陷入局部最优解。本文通过求解TSP问题,对其进行改进。通过在特定情况下对路径进行逐步遍历比较来降低陷入局部最优解的可能性,找出最优解。实验验证结果表明,这种改进蚁群算法对求解TSP问题有较好的效果。
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| 关 键 词： | 蚁群算法 TSP 改进 遍历 |
| 收稿时间： | 2009-12-10 |
An Ant Colony Algorithm Based Improvement for TSP Solutions |
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| CHEN Jie,LIU Xi-yu,LI Qing-bo.An Ant Colony Algorithm Based Improvement for TSP Solutions[J].Shandong Science,2010,23(2):58-61. |
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| Authors: | CHEN Jie LIU Xi-yu LI Qing-bo |
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| Affiliation: | 1. School of Management and Economics, Shandong Normal University, Jinan 250014, China;
2. Shandong Miky Way Information Technology Co. Ltd., Jinan 250100, China |
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| Abstract: | The ant colony algorithm has such positives as robustness and capability to discover better solutions,while it has also such negatives as longer search time and easy local optimal solution falling when larger-scale search is performed.This paper improves the solutions for TSP problems by gradual ergodic path comparisons in some specific conditions to reduce the possibilities to fall into local optimal solutions.Experimental results show that the improved ant colony algorithm has better effectiveness for TSP... |
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| Keywords: | TSP |
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