An advanced Genetic Algorithm for Traveling Salesman Problem

被引：1

作者：

Wang Youping ^{[1
]}

Li Liang ^{[2
]}

Chen Lin ^{[1
]}

机构：

[1] Yangtze Univ, Coll Comp Sci, Jinzhou 434023, Hubei, Peoples R China

[2] Yangtze Univ, Coll Informat & Math, Hubei 434023, Peoples R China

来源：

THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING | 2009年

关键词：

Genetic algorithm; crossover; heuristic knowledge; mutation;

D O I：

10.1109/WGEC.2009.127

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

By analyzing the deficiency of traditional genetic algorithm in solving the Traveling Salesman Problem, an improved genetic algorithm is proposed for TSP. In this paper, the ordinal real-number encoder is used for chromosome encoding and ordered crossover operators is advanced that utilizes local and global information to construct offspring. In order to guarantee global convergence, heuristic knowledge and self-learning is employed for mutation. Then, a city network which contains 31 city nodes is employed to test the algorithm. The simulation result of MATLAB shows that the proposed method can get feasible result with a higher convergent rate and success rate than existing heuristics.

引用

页码：101 / +

页数：2