Ant colony algorithm based on genetic method for continuous optimization problem

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 p...

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Published inJournal of Shanghai University Vol. 11; no. 6; pp. 597 - 602
Main Author 朱经纬 蒙陪生 王乘
Format Journal Article
LanguageEnglish
Published 01.12.2007
Subjects
蚁群算法
连续优化
遗传机制
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ISSN1007-6417
1863-236X
DOI10.1007/s11741-007-0614-1

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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.
AbstractList 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.
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.
Author 朱经纬 蒙陪生 王乘
AuthorAffiliation Department of Mechanics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
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Notes ant colony algorithm, genetic method, diffusion function, continuous optimization problem.
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Snippet 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...
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SubjectTerms 蚁群算法
连续优化
遗传机制
Title Ant colony algorithm based on genetic method for continuous optimization problem
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