| 基于有向搜索的蚁群算法及其仿真 |
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| 引用本文： | 陈佩树,于霜.基于有向搜索的蚁群算法及其仿真[J].巢湖学院学报,2011(6):1-4. |
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| 作者姓名： | 陈佩树 于霜 |
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| 作者单位： | 1. 巢湖学院数学系,安徽巢湖,238000
2. 江苏大学电气信息工程学院,江苏镇江,212013 |
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| 基金项目： | 安徽省2010年高校省级优秀青年人才基金项目，2011年度高校省级科学研究项目，巢湖学院科研启动基金 |
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| 摘 要： | 为了解决蚁群算法收敛速度慢和易陷入局部最优的问题,提高算法在连续空间中的寻优能力,本文提出了一种基于有向搜索的智能蚁群优化算法。该算法使转移概率较大的蚂蚁个体在解空间中进行局部有向变步长搜索,有效地避免了算法陷入局部最优,缩短了搜索时间,在寻优精确度取得了很好的效果。通过仿真验证了算法的有效性
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| 关 键 词： | 蚁群算法 有向搜索 变步长 |
ANT COLONY OPTIMIZATION AND SIMULATION BASED ON DIRECTED SEARCH |
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| CHEN Pei-shu,YU Shuang.ANT COLONY OPTIMIZATION AND SIMULATION BASED ON DIRECTED SEARCH[J].Chaohu College Journal,2011(6):1-4. |
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| Authors: | CHEN Pei-shu YU Shuang |
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| Institution: | 1 Department of Mathematics,Chaohu University,Chaohu Anhui 238000)(2 School of Electrical and Information Engineering,Jiangsu University,Zhenjiang Jiangsu 212013) |
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| Abstract: | Ant colony algorithm has slow convergence speed and is easy to fall in local optimal, in order to overcome these shortcomings and to improve ability of ant colony optimization, a new algorithm based on directed search is proposed. The pro- posed algorithm applies directed and variable step length to the ant which has bigger probability in local space. It avoids ant colony trapped in local optimal. The algorithm costs shorter time and gets better accuracy than common ant colony algorithm. Simulate results are given to show the validity of the proposed algorithm. |
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| Keywords: | ant colony algorithm directed search variable step length |
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