基于遗传机制的蚁群算法求解连续优化问题(英文) 被引量：1

Ant colony algorithm based on genetic method for continuous optimization problem

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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. 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.

作者朱经纬蒙培生王乘

机构地区 Department of Mechanics

出处《Journal of Shanghai University(English Edition)》 CAS 2007年第6期597-602,共6页 上海大学学报（英文版）

基金 project supported by the National High-Technology Research and Development Program of China(Grant No.8632005AA642010)

关键词 ant colony algorithm genetic method diffusion function continuous optimization problem. ant colony algorithm, genetic method, diffusion function, continuous optimization problem.

分类号 TP301.6 [自动化与计算机技术—计算机系统结构]

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