Improved ant colony algorithm and its applications in TSP

被引：0
作者：
Song, Xuemei [1 ]
Li, Bing [2 ]
Yang, Hongmei [3 ]
机构：
[1] Hebei Polytech Univ, Comp & Automat Control Sch, Hebei 063009, Peoples R China
[2] Tangshan Coll, Hebei 063009, Peoples R China
[3] Hebei Polytech Univ, Coll Continuing Educ, Hebei 063009, Peoples R China
来源：
ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 2 | 2006年
关键词：
ant colony optimization; Traveling Salesman Problem;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the fields of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques. A kind of improved ACO (named PMACO) approach for traveling salesman problems (TSP) is presented. Aimed at the disadvantages existed in ACO, several new betterments are proposed and evaluated. In particular, the option that an ant hunts for the next step, the use of a combination of two kinds of pheromone evaluation models, the change of amount in the ant colony during the run of the algorithm, and the mutation of pheromone are studied. We tested ACO algorithm on a set of benchmark problems from the Traveling Salesman Problem Library. It performed better than the original and the other improved ACO algorithms.
引用
收藏
页码：1145 / +
页数：2
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