A Neuro-Immune Network for Solving the Traveling Salesman Problem

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-...

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Bibliographic Details
Published inThe 2006 IEEE International Joint Conference on Neural Network Proceedings pp. 3760 - 3766
Main Authors Pasti, R., de Castro, L.N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Annealing
Artificial neural networks
Benchmark testing
Cities and towns
Cost function
Immune system
Intelligent systems
Neural networks
Optimization methods
Traveling salesman problems
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Summary: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.
ISBN:9780780394902
0780394909
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2006.247394