DocumentCode
2837211
Title
An improved neural network approach to the traveling salesman problem
Author
Gowda, Sudhir M. ; Lee, Bang W. ; Sheu, Sing J.
Author_Institution
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
fYear
1989
fDate
22-24 Nov 1989
Firstpage
552
Lastpage
555
Abstract
To find the global minimum of an NP-complete problem within a reasonable computational time is extremely difficult. The traveling salesman problem, in addition to being NP-complete, has a complicated solution set in terms of optimizing an energy function. A novel neural network that removes ambiguities in the solution set and eliminates local minima is described. This network obtains the global minimum at a small increase in computational time when compared to the Hopfield network. Salient features of this improved network are presented
Keywords
neural nets; operations research; optimisation; NP-complete problem; combinatorial optimisation; competitive networks modified Hopfield network; fixed starting city; global minimum; neural network; traveling salesman problem; Cities and towns; Computational complexity; Computer networks; Equations; Hopfield neural networks; NP-complete problem; Neural engineering; Neural networks; Polynomials; Traveling salesman problems;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON '89. Fourth IEEE Region 10 International Conference
Conference_Location
Bombay
Type
conf
DOI
10.1109/TENCON.1989.177000
Filename
177000
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