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蚁群优化算法求解TSP问题研究 被引量：2

Research on Ant Colony Optimization Algorithm Solving TSP
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摘要 介绍了信息素混合更新的蚁群优化算法,并用来求解TSP问题。混合信息素更新的蚁群优化算法是在蚁群系统(ACS)的基础上改进而成的,它在演化过程中,通过改变信息素的迭代最优更新规则和全局最优更新规则的使用频率,逐渐增加全局最优更新规则的使用频率,从而提高系统收敛的速度和减少系统搜索的导向性,并以O liver30和att48为例给出了实验结果,说明了该混合算法的有效性。 The ant colony optimization of pheromone mixed rule is introduced, and is used to solve the traveling salesman problems（TSP）. The ant colony optimization algorithm based on mixed pheromone update is modified based on ant colony system （ACS）, in its evolutionary process, by use of changing the frequency of iterative optimization update rule and overall optimization update rule of the pheromone, gradually increases the frequency of overall update rule, to enhances the speed of convergence and the guidance of search, And takeing Oliver30 and att48 as example, the simulation results show the validity of this algorithm.
作者 王艳玲 李龙澍
机构地区 安徽大学计算机科学与技术学院
出处 《计算机与现代化》 2008年第7期85-87,共3页 Computer and Modernization
关键词 信息素 蚁群优化算法 蚁群系统 pheromone ant colony optimization ant colony system
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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参考文献14

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共引文献196

同被引文献13

  • 1杜天军,陈光,刘占辰.多目标攻击空战决策WBG模型及其蚁群算法[J].系统工程与电子技术,2005,27(5):861-865. 被引量：15
  • 2付宇,肖健梅.动态自适应蚁群算法求解TSP问题[J].计算机辅助工程,2006,15(4):10-13. 被引量：5
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计算机与现代化
2008年 第7期

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