[1]张代远[].一类新型改进的广义蚁群优化算法[J].计算机技术与发展,2012,(06):39-44.
　ZHANG Dai-yuan.A New Improved Generalized Ant Colony Optimization Algorithm[J].,2012,(06):39-44.
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一类新型改进的广义蚁群优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年06期
页码:
39-44
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
A New Improved Generalized Ant Colony Optimization Algorithm
文章编号:
1673-629X（2012）06-0039-06
作者:
张代远[1; 2; 3]
[1]南京邮电大学计算机学院[2]江苏省无线传感网高技术研究重点实验室[3]南京邮电大学计算机技术研究所
Author(s):
ZHANG Dai-yuan
[1]College of Computer, Nanjing University of Posts and Telecommunications[2]Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks[3]Institute of Computer Technology, Nanjing University of Posts and Telecommunications
关键词:
人工智能; 蚁群优化算法; 收敛性; 信息素更新规则
Keywords:
artificial intelligence;  ant colony optimization algorithm ; convergence;  pheromone update rule
分类号:
TP31
文献标志码:
A
摘要:
提出了一类新型蚁群优化算法。该算法改进了概率选择函数,将概率选择函数由严格单调增函数推广为有界函数,给出了蚂蚁在某一源节点选择下一个节点的更一般的表达式。证明了算法收敛的重要定理：即对足够大的迭代次数,改进的广义蚁群优化算法至少找到最优解一次的概率趋近于1。提出了信息素渐近平衡原理。在信息素更新规则中,引入了信息素残留率函数、信息素增量函数。证明了渐近信息素在最优路径上将会趋于一个正数,而在非最优路径上将会趋于0。最后,计算机仿真实验结果表明,无论是获得的最优解的质量还是算法的收敛速度,文中提出的改进的广义蚁群优化算法都优于传统的蚁群优化算法
Abstract:
A new improved generalized ant colony optimization algorithm （ IGACO ） is proposed in this paper. The selected probability functions are generalized from strictly increasing continuous functions to bounded functions, which gives a more general form of expression for the probability of selecting the next node. An important theorem is proved for describing the convergence of IGACO algorithm, i.e. for a sufficiently large number of algorithm iterations, the probability of finding the globally optimal solution at least once tends to 1. A principle of pheromone asymptotic balance is proposed. In the pheromone update rule,the residual rate function of pheromone and the global increasing function of pheromone are presented. Prove that the residual pheromone tends to a positive number on the edges that are globally optimal solution, and tends to 0 on the edges that are not globally optimal solution. Finally, the computational simulation shows that,compared with traditional ant colony optimization algorithm,the IGACO algorithm has good performance both on globally optimal solution and convergent speed

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备注/Memo

备注/Memo:
江苏高校优势学科建设工程资助项目（yx002001）张代远（1957-），男，教授，博土，研究生导师，研究方向为人工智能、计算机体系结构、计算机应用等
更新日期/Last Update: 1900-01-01