Diversity maintenance is an important aspect in population-based metaheuristics for optimization, as it tends to allow a better exploration of the search space, thus reducing the susceptibility to local optima in multimodal optimization problems. In this context, metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though generally implementing very simple mechanisms to control the dynamics of the network. To increase such diversity maintenance capability even further, a new immune-inspired algorithm was recently proposed, which adopted a novel concentration-based model of immune network. This new algorithm, named cob-aiNet (Concentration-based Artificial Immune Network), was originally developed to solve real-parameter single-objective optimization problems, and it was later extended (with cob-aiNet[MO]) to deal with real-parameter multi-objective optimization. Given that both cob-aiNet and cob-aiNet[MO] obtained competitive results when compared to state-of-the-art algorithms for continuous optimization and also presented significantly improved diversity maintenance mechanisms, in this work the same concentration-based paradigm was further explored, in an extension of such algorithms to deal with single-objective combinatorial optimization problems. This new algorithm, named cob-aiNet[C], was evaluated here in a series of experiments based on four Traveling Salesman Problems (TSPs), in which it was verified not only the diversity maintenance capabilities of the algorithm, but also its overall optimization performance. © 2011 IEEE. 12421249De Castro, L.N., (2006) Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, Ser. Chapman & Hall/CRC Computer & Information Science Series, , Chapman & Hall/CRCDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer VerlagJerne, N.K., Towards a network theory of the immune system (1974) Annales d'immunologie, 125 (1-2), pp. 373-389De França, F.O., Coelho, G.P., Castro, P.A.D., Von Zuben, F.J., Conceptual and practical aspects of the aiNet family of algorithms (2010) International Journal of Natural Computing Research, 1 (1), pp. 1-35De França, F.O., Coelho, G.P., Von Zuben, F.J., On the diversity mechanisms of opt-aiNet: A comparative study with fitness sharing (2010) Proc. of the 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 3523-3530Coelho, G.P., Von Zuben, F.J., A concentration-based artificial immune network for continuous optimization (2010) Proc. of the 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 108-115Coelho, G.P., Von Zuben, F.J., A concentration-based artificial immune network for multi-objective optimization (2011) Proc. of the 6th. International Conference on Evolutionary Multi-Criterion Optimization (EMO), Ser. Lecture Notes in Computer Science, 6576, pp. 343-357. , Springer Berlin/HeidelbergApplegate, D.L., Bixby, R.E., Chvátal, V., (2006) The Traveling Salesman Problem: A Computational Study, Ser. Princeton Series in Applied Mathematics, , W. J. Cook, Princeton University PressLawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B., (1985) The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, , ser. Wiley-Interscience series in discrete mathematics and optimization. WileyTSPLIB - A Traveling Salesman Problem Library, , http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95Burnet, F.M., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , G. I. Bell, A. S. Perelson, and G. H. Pimgley Jr, Eds. Marcel Dekker IncBersini, H., Revisiting Idiotypic Immune Networks (2003) Lecture Notes in Computer Science, (2801), pp. 164-174. , Advances in Artificial LifeBersini, H., Self-assertion vs self-recognition: A tribute to Francisco Varela (2002) Proc. of the 1st International Conference on Artificial Immune Systems (ICARIS), pp. 107-112Lin, S., Kernighan, B.W., An effective heuristic algorithm for the traveling-salesman problem (1973) Operations Research, 21 (2), pp. 498-516De Franca, F.O., Gomes, L.C.T., De Castro, L.N., Von Zuben, F.J., Handling time-varying TSP instances (2006) 2006 IEEE Congress on Evolutionary Computation, CEC 2006, pp. 2830-2837. , 1688664, 2006 IEEE Congress on Evolutionary Computation, CEC 2006Prokopec, A., Marin, G., Adaptive mutation operator cycling (2009) Proc. of the 2nd Intl. Conference on the Applications of Digital Information and Web Technologies (ICADIWT), pp. 661-666