Particle swarm optimization-based algorithms for TSP and generalized TSP

被引：304
|
作者：
Shi, X. H.
Liang, Y. C. [1 ]
Lee, H. P.
Lu, C.
Wang, Q. X.
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
[2] Inst High Performance Computat, Singapore 117528, Singapore
[3] Natl Univ Singapore, Dept Mech Engn, Singapore 119260, Singapore
来源：
INFORMATION PROCESSING LETTERS | 2007年 / 103卷 / 05期
基金：
中国国家自然科学基金;
关键词：
algorithms; particle swarm optimization; traveling salesman problem; generalized traveling salesman problem; swap operator;
D O I：
10.1016/j.ipl.2007.03.010
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented. An uncertain searching strategy and a crossover eliminated technique are used to accelerate the convergence speed. Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by employing the generalized chromosome. Two local search techniques are used to speed up the convergence. Numerical results show the effectiveness of the proposed algorithms. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码：169 / 176
页数：8
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