An improved immune-genetic algorithm for the traveling salesman problem

被引：0
|
作者：
Lu, Jingui [1 ]
Fang, Ning [2 ]
Shao, Dinghong [1 ]
Liu, Congyan [1 ]
机构：
[1] Nanjing Univ Technol, Nanjing 210009, Jiangsu Provinc, Peoples R China
[2] Utah State Univ, Engn Coll, Logan, UT 84322 USA
来源：
ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 4, PROCEEDINGS | 2007年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An improved immune-genetic algorithm is applied to solve the Traveling Salesman Problem (TSP) in this paper. A new selection strategy is incorporated into the conventional genetic algorithm to improve the performance of genetic algorithm. The selection strategy includes three computational procedures: evaluating the diversity of genes, calculating the percentage of genes, and computing the selection probability ofgenes. Computer numerical experiments on two case studies (21-city and 56-city TSPs) are performed to validate the effectiveness of the improved immune-genetic algorithm. The results show that by incorporating inoculating genes into conventional procedures of genetic algorithm, the number of evolutional iterations to reach an optimal solution can be significantly reduced.
引用
收藏
页码：297 / +
页数：3
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