DocumentCode :
290286
Title :
Approach of using a density equalizing function to self-organizing learning for solving travelling salesman problem
Author :
Choy, Clifford Sze-Tsan ; Siu, Wan-chi
Author_Institution :
Dept. of Electron. Eng., Hong Kong Polytech., Kowloon, Hong Kong
Volume :
ii
fYear :
1994
fDate :
19-22 Apr 1994
Abstract :
Proposes a new approach which requires neither neuron addition nor deletion, and at the same time, N neurons are sufficient to solve an N-city travelling salesman problem. the authors begin with a description of their model, and then results for applying the model to solve the 30-city problem from Hopfield are presented. Results of practical testing show that the present approach always converges. It has the highest chance to achieve the optimal solution, and gives the best most probable solution, as compared to other self-organizing algorithms
Keywords :
combinatorial mathematics; minimisation; self-organising feature maps; travelling salesman problems; 30-city problem; convergence; density equalizing function; most probable solution; optimal solution; practical testing; self-organizing learning; travelling salesman problem; Cities and towns; Cost function; Network topology; Neural networks; Neurons; Simulated annealing; Testing; Traveling salesman problems; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
ISSN :
1520-6149
Print_ISBN :
0-7803-1775-0
Type :
conf
DOI :
10.1109/ICASSP.1994.389589
Filename :
389589
Link To Document :
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