A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem

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Information Sciences
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Masutti T.A.S.
de Castro L.N.
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Most combinatorial optimization problems belong to the NP-complete or NP-hard classes, which means that they may require an infeasible processing time to be solved by an exhaustive search method. Thus, less expensive heuristics in respect to the processing time are commonly used. These heuristics can obtain satisfactory solutions in short running times, but there is no guarantee that the optimal solution will be found. Artificial Neural Networks (ANNs) have been widely studied to solve combinatorial problems, presenting encouraging results. This paper proposes some modifications on RABNET-TSP, an immune-inspired self-organizing neural network, for the solution of the Traveling Salesman Problem (TSP). The modified algorithm is compared with other neural methods from the literature and the results obtained suggest that the proposed method is competitive in relation to the other ones, outperforming them in many cases with regards to the quality (cost) of the solutions found, though demanding a greater time for convergence in many cases. © 2008 Elsevier Inc. All rights reserved.
Assuntos Scopus
Artificial immune systems , Artificial neural networks , Combinatorial op-timization problems , Combinatorial problems , Exhaustive searches , Immune systems , Modified algorithms , NP-complete , NP-hard class , Optimal solutions , Processing Time , Running time , Self-or-ganizing neural networks , Self-organizing networks
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