Integrating the best 2-opt method to enhance the genetic algorithm execution time in solving the traveler salesman problem

被引：0

作者：

Sabba, Sara ^{[1
]}

Chikhi, Salim ^{[1
]}

机构：

[1] Computer Science Department, MISC Laboratory, Mentouri University-Constantine, Algeria

来源：

Advances in Intelligent and Soft Computing | 2012年 / 170 AISC卷

关键词：

Combinatorial optimization - Traveling salesman problem - Problem solving;

D O I：

10.1007/978-3-642-30662-4-13

中图分类号：

学科分类号：

摘要：

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problem NP-complete that requires much time to find the good solution. Indeed, the genetic algorithm is a stochastic optimization algorithm; it is to find an approximate solution of a hard problem. However, genetic algorithm has a great tendency to converge to a local minimum and stay stuck in adverse solutions. To solve this problem, we study in this paper the impact of the integration of a new local optimization heuristic Best 2-opt with the genetic operators on the quality of solution and the runtime of the GA. The hybridization proposed was tested on instances from 29 to 246 cities. The obtained results are very satisfied regarding to the solution qualities and the execution time. © 2012 Springer-Verlag Berlin Heidelberg.

引用

页码：195 / 208