Journal
INFORMATION SCIENCES
Volume 179, Issue 10, Pages 1454-1468Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2008.12.016
Keywords
Traveling salesman problem; Self-organizing networks; Artificial immune systems; Artificial neural networks; Combinatorial optimization
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Funding
- CNPq
- Fapesp
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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. (c) 2008 Elsevier Inc. All rights reserved.
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