| Full text | |
| Author(s): |
Pasti, Rodrigo
;
de Castro, Leandro Nunes
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6; v. N/A, p. 2-pg., 2006-01-01. |
| Abstract | |
Many combinatorial optimization problems belong to the NP class and, thus, cannot be solved optimally in feasible time using standard techniques (e.g., enumeration methods). NP problems have been tackled with some success by techniques known as meta-heuristics. The present paper proposes a new meta-heuristics for solving traveling salesman problems (TSP) based on a neural network trained using ideas from the immune system. The network is self-organized and the learning algorithm aims at locating one network cell at each position of a city of the TSP instance to be solved. The pre-defined network neighborhood is going to establish the final route proposed for the TSP. The algorithm is applied to several instances from the literature and the results compared with the best solutions available. (AU) | |