Solving TSP Problem with Improved Genetic Algorithm

被引：3

作者：

Fu, Chunhua ^{[1
]}

Zhang, Lijun ^{[1
]}

Wang, Xiaojing ^{[1
]}

Qiao, Liying ^{[1
]}

机构：

[1] China Agr Means Prod Assoc, Beijing, Peoples R China

来源：

6TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2018) | 2018年 / 1967卷

关键词：

genetic algorithm; improvement; encode; selection; crossover; mutation;

D O I：

10.1063/1.5039131

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

引用

页数：6

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