Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques

被引:189
作者
Chen, Shyi-Ming [1 ,2 ]
Chien, Chih-Yao [1 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan
[2] Natl Taichung Univ Educ, Grad Inst Educ Measurement & Stat, Taichung, Taiwan
关键词
Traveling salesman problem; Genetic algorithms; Ant colony systems; Simulated annealing; Particle swarm optimization; Genetic simulated annealing ant colony system with particle swarm optimization techniques; NEURAL-NETWORK; HYBRID; ALGORITHM;
D O I
10.1016/j.eswa.2011.04.163
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we present a new method, called the genetic simulated annealing ant colony system with particle swarm optimization techniques, for solving the traveling salesman problem. We also make experiments using the 25 data sets obtained from the TSPLIB (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) and compare the experimental results of the proposed method with the methods of Angeniol, Vaubois, and Texier (1988), Somhom, Modares, and Enkawa (1997), Masutti and Castro (2009) and Pasti and Castro (2006). The experimental results show that both the average solution and the percentage deviation of the average solution to the best known solution of the proposed method are better than the methods of Angeniol et al. (1988), Somhom et al. (1997), Masutti and Castro (2009) and Pasti and Castro (2006). (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:14439 / 14450
页数:12
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