Fuzzy Ant Colony Optimization algorithm for Shortest Path Problem under uncertain environment

被引：0
|
作者：
Yin, Wenshuang [1 ]
Wei, Daijun [1 ]
机构：
[1] School of Science, Hubei University for Nationalities, No. 39, Xueyuan Road, Enshi 445000, China
来源：
ICIC Express Letters | 2014年 / 8卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Artificial intelligence - Fuzzy rules - Ant colony optimization - Graph theory;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
How to solve the Shortest Path Problem (SPP) under uncertain environment is an open issue. In this paper, a generalized ant colony algorithm is proposed to deal with SPP under uncertain environment. First, we add each fuzzy number of two connected edges together. Second, by adopting the graded mean integration representation of fuzzy numbers, we can compare the distance between two different paths with their fuzzy path lengths. The proposed Fuzzy Ant Colony Optimization (FACO) algorithm can practically solve the SPP under fuzzy environment, especially in large-scale networks. A numerical example of a transportation network is provided to demonstrate the efficiency of the proposed method. © 2014 ISSN 1881-803X.
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页码：1839 / 1844
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