| 基于遗传机制的蚁群算法求解连续优化问题 |
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| 引用本文： | 朱经纬,蒙陪生,王乘.基于遗传机制的蚁群算法求解连续优化问题[J].上海大学学报(英文版),2007,11(6):597-602. |
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| 作者姓名： | 朱经纬 蒙陪生 王乘 |
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| 作者单位： | Department of Mechanics Huazhong University of Science and Technology，Department of Mechanics，Huazhong University of Science and Technology，Department of Mechanics，Huazhong University of Science and Technology，Wuhan 430074，P.R.China，Wuhan 430074，P.R.China，Wuhan 430074，P.R.China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem.Each component has a seed set.The seed in the set has the value of component,trail information and fitness.The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed.The genetic method is used to form new solutions from the solutions got by the ants.Best solutions are selected to update the seeds in the sets and trail information of the seeds.In updating the trail information,a diffusion function is used to achieve the diffuseness of trail information.The new algorithm is tested with 8 different benchmark functions.
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| 关 键 词： | 遗传机制 蚁群算法 求解 连续优化 |
| 收稿时间： | 10 July 2006 |
| 修稿时间： | 2006-07-10 |
Ant colony algorithm based on genetic method for continuous optimization problem |
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| ZHU Jing-wei,MENG Pei-sheng,WANG Cheng.Ant colony algorithm based on genetic method for continuous optimization problem[J].Journal of Shanghai University(English Edition),2007,11(6):597-602. |
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| Authors: | ZHU Jing-wei MENG Pei-sheng WANG Cheng |
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| Institution: | Department of Mechanics, Huazhong University of Science and Technology, Wuhan 430074,P.R.China |
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| Abstract: | A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem.Each component has a seed set.The seed in the set has the value of component,trail information and fitness.The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed.The genetic method is used to form new solutions from the solutions got by the ants.Best solutions are selected to update the seeds in the sets and trail information of the seeds.In updating the trail information,a diffusion function is used to achieve the diffuseness of trail information.The new algorithm is tested with 8 different benchmark functions. |
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| Keywords: | ant colony algorithm genetic method diffusion function continuous optimization problem |
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