Neural network methods in combinatorial optimization
Computers and Operations Research - Special issue on neural networks and operations research
The guilty net for the traveling salesman problem
Computers and Operations Research - Special issue on neural networks and operations research
Computers and Operations Research
Competition-based neural network for the multiple travelling salesmen problem with minmax objective
Computers and Operations Research - Special issue on the traveling salesman problem
Case injected genetic algorithms for traveling salesman problems
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
How to solve it: modern heuristics
How to solve it: modern heuristics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Elements of the Theory of Computation
Elements of the Theory of Computation
Self-Organizing Maps
An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem
Neural Processing Letters
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Neural network approach to solving the Traveling Salesman Problem
Journal of Computing Sciences in Colleges
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
On model design for simulation of collective intelligence
Information Sciences: an International Journal
An improved approximation algorithm for the maximum TSP
Theoretical Computer Science
Information Sciences: an International Journal
Information Sciences: an International Journal
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
Inspection planning in the polygonal domain by Self-Organizing Map
Applied Soft Computing
An artificial immune system based algorithm to solve unequal area facility layout problem
Expert Systems with Applications: An International Journal
Solving the traveling salesman problem using cooperative genetic ant systems
Expert Systems with Applications: An International Journal
A cooperative ant colony system and genetic algorithm for TSPs
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A discrete artificial bee colony algorithm for TSP problem
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A novel bio-inspired approach based on the behavior of mosquitoes
Information Sciences: an International Journal
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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.