| 一种基于信息素变化的改进蚁群算法 |
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| 引用本文： | 刘海军,彭绍雄,高传斌,邹强.一种基于信息素变化的改进蚁群算法[J].兵工自动化,2012,31(4):28-31. |
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| 作者姓名： | 刘海军 彭绍雄 高传斌 邹强 |
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| 作者单位： | 海军航空工程学院研究生管理大队,山东烟台,264001;海军航空工程学院飞行器工程系,山东烟台,264001 |
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| 摘 要： | 针对蚁群算法搜索时间长、易陷于局部最优解的缺点,提出一种增幅递减的局部信息素更新模型。通过分析现有蚁群算法信息素更新模型陷入局部最优的原因,借鉴蚁群模型退火算法思想,根据假设推导出增幅递减信息素更新模型,分析该模型对算法复杂度的影响,并分别采用4种信息素更新模型求解最短路问题。仿真结果表明,该模型能较好地抑制算法陷入局部最优解问题。
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| 关 键 词： | 蚁群算法 增幅递减 局部最优 信息素变化 |
| 收稿时间： | 2013/3/6 0:00:00 |
An Improved Ant Colony Algorithm Based on Pheromone Changing |
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| Liu Haijun
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Peng Shaoxiong
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Gao Chuanbin
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Zou Qiang.An Improved Ant Colony Algorithm Based on Pheromone Changing[J].Ordnance Industry Automation,2012,31(4):28-31. |
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| Authors: | Liu Haijun Peng Shaoxiong Gao Chuanbin Zou Qiang |
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| Affiliation: | 1. Administrant Brigade of Postgraduate, Naval Aeronautical ＆ Astronautical University, Yantai 264001, China; 2. Dept. of Aircraft Engineering, Naval Aeronautical ＆ Astronautical University, Yantai 264001, China) |
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| Abstract: | The ant colony algorithm search time is long and it is easy to fall into the local optimal. Put forward the amplitude descending local phenomenon renovating model. Through analyzing why the present algorithm fall into the local optimal, and using ant colony recombining algorithm, and according to hypothesis deduce amplitude descending local phenomenon renovating model, and analyze influence of model on algorithm complexity. Then use four pheromone renovating models to solve the shortest path problems. The simulation result shows that the model can restrain the algorithm to fall into the local optimal. |
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| Keywords: | ant colony algorithm amplitude descending local optimal problem growth changing |
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