[1]丛望,张敬南,吴盼良.电网节点编号优化的一种改进蚁群算法[J].应用科技,2008,(12):23-26.
 CONG Wang,ZHANG Jing-nan,WU Pan-liang.An improved ant colony algorithm in electric network node numbering optimization[J].yykj,2008,(12):23-26.
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电网节点编号优化的一种改进蚁群算法
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
期数:
2008年12期
页码:
23-26
栏目:
自动化技术
出版日期:
2008-12-05

文章信息/Info

Title:
An improved ant colony algorithm in electric network node numbering optimization
文章编号:
1009-671X(2008)12-0023-04
作者:
丛望张敬南吴盼良
哈尔滨工程大学自动化学院,黑龙江哈尔滨 150001
Author(s):
CONG Wang ZHANG Jing-nan WU Pan-liang
College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
蚁群算法电力网络节点编号优化最大最小蚁群系统
Keywords:
ant colony algorithm electric power network node numbering optimization max-min ant colony system
分类号:
TM744
文献标志码:
A
摘要:
蚁群算法是近几年优化领域中新出现的一种启发式仿生类并行智能进化系统, 目前已经在众多组合优化领域中得到广泛应用.不同于传统的节点编号优化方法,采用最大最小蚁群系统改进的蚁群算法能快速地找到多个全局最优解,并且不易陷入局部最优解.将多种蚁群算法的改进融合在一起,取长补短,得到了较满意的效果.根据节点编号本身的特点,通过引入初始节点选择开关算子,同时在迭代过程中不断淘汰劣势蚂蚁,使蚂蚁能更快地找到最优解.
Abstract:
Ant colony algorithm is a novel category of bionic metaheuristic system, and has wide application in various combined optimization fields. Different from traditional methods of node numbering optimization, the most global optimal solutions can be found by improved ant colony algorithms based on maxmin ant system, and without dropping in local optimal solutions. Besides, satisfactory result will be obtained if some ant algorithms are used to benefit each other. According to traits of node numbering, a switch operator to choose initial nodes is introduced, and the ants in weaker traces are knocked out in every iterative process, which make the optimal solutions fount out by ants more quickly.

参考文献/References:

[1]王锡凡.现代电力系统分析\[M\].北京:科学出版社,2003.
[2] 彭春华,徐雪松.基于蚁群算法的电力网络节点编号多方案优化[J].电力系统及其自动化学报,2007,19(2): 61-65. 
[3]段海滨,王道波,于秀芬. 蚁群算法的研究现状及其展望[J]. 中国工程科学, 2007(02): 98-102.
[4]DORIGO M, MANIEZZO V, COLORNI A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transaction on Systems, Man, and Cybernetics Part B, 1996, 26(1): 29-41.
[5] DORIGO M, BIRATTARI M, STUTZLE T. Ant colony optimization artificial ants as a computational intelligence technique\[J\]. IEEE Computational Intelli- gence Magazine, 2006(4):28-39.
[6]张毅,梁艳春. 基于选路优化的改进蚁群算法[J]. 计算机工程与应用, 2007,43(2):60-63.
 [7]STUETZLE T, Hoos H. MAX-MIN ant system and local search for the traveling salesman problem[C]// Proceedings of the IEEE Conference on Evolutionary Computation Indianapolis.[s.l],1997: 309-314.

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备注/Memo

备注/Memo:
丛望(1958-), 男,教授, 主要研究方向:电力系统及其自动化,E-mail:cwcyenetease.com.
更新日期/Last Update: 2008-12-18