[1]王一清,宋爱国,黄惟一.基于Bayes决策的蚁群优化算法[J].东南大学学报(自然科学版),2005,35(4):558-562.[doi:10.3969/j.issn.1001-0505.2005.04.014]
 Wang Yiqing,Song Aiguo,Huang Weiyi.Ant colony optimal algorithms based on Bayes decision[J].Journal of Southeast University (Natural Science Edition),2005,35(4):558-562.[doi:10.3969/j.issn.1001-0505.2005.04.014]
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基于Bayes决策的蚁群优化算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
35
期数:
2005年第4期
页码:
558-562
栏目:
计算机科学与工程
出版日期:
2005-07-20

文章信息/Info

Title:
Ant colony optimal algorithms based on Bayes decision
作者:
王一清12 宋爱国1 黄惟一1
1 东南大学仪器科学与工程系, 南京 210096; 2 徐州供电公司调度中心, 徐州 221000
Author(s):
Wang Yiqing12 Song Aiguo1 Huang Weiyi1
1 Department of Instrument Science and Technology, Southeast University, Nanjing 210096, China
2 Center of Dispatching, Xuzhou Power Supply Company, Xuzhou 221000, China
关键词:
蚁群算法 Bayes决策 极大熵 先验分布 后验分析
Keywords:
ant colony algorithms Bayes decision maximum entropy prior distribution posterior analysis
分类号:
TP301.6
DOI:
10.3969/j.issn.1001-0505.2005.04.014
摘要:
基于Bayes决策理论,提出了一种可以改进蚁群算法搜索性能的有效方法; 针对基本蚁群算法中存在的“停滞”现象,对蚂蚁个体的寻优过程采取了隔代强化的措施,使算法具备较强的发现新解的能力,再采用后验分析对蚁群算法中的转移概率进行调整,使得改进后的蚁群算法在随机搜索过程中呈现出自组织特性,蚂蚁个体利用各自的后验知识不断地强化那些能“经受考验”的可行解,从而有效地压缩了搜索空间,提高了搜索效率.试验结果表明,该方法无需知道转移概率的先验分布,在解空间的全局寻优时具有良好的收敛性和鲁棒性.
Abstract:
An effective method based on the principle of Bayes decision is put forth to improve the searching performance of basic ant colony algorithms. Aiming at the stagnation phenomenon, a way of interval strengthening is applied, thus the new method has a good ability of finding new solution. Meanwhile, posterior analysis is adopted to adjust the diversion probability and is applied by each agent to strengthen those durable solutions, which makes the stochastic searching process of the modified algorithms appear self-organizing characteristics and reduce the hunting sphere largely and improve the searching efficiency. The results of experiment show that the proposed method, even without any knowledge of diversion probability’s prior distribution, has favorable convergence and robustness in finding the optimal solution.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金资助项目(69875004)、江苏省自然科学基金资助项目(BK2001402).
作者简介: 王一清(1973—),男,博士; 黄惟一(联系人),男,教授,博士生导师,hhwy@seu.edu.cn.
更新日期/Last Update: 2005-07-20