A Dynamic Artificial Immune Algorithm Applied To Challenging Benchmarking Problems

In many real-world scenarios, in contrast to standard benchmark optimization problems, we may face some uncertainties regarding the objective function. One source of these uncertainties is a constantly changing environment in which the optima change their location over time. New heuristics or adaptations to already available algorithms must be conceived in order to deal with such problems. Among the desirable features that a search strategy should exhibit to deal with dynamic optimization are diversity maintenance, a memory of past solutions, and a multipopulation structure of candidate solutions. In this paper, an immune-inspired algorithm that presents these features, called dopt-aiNet, is properly adapted to deal with six newly proposed benchmark instances, and the obtained results are outlined according to the available specifications for the competition at the Congresson Evolutionary Computation 2009. © 2009 IEEE.423430Bazaraa, M.S., Sherali, H.D., Shetty, C.M., (2006) Nonlinear Programming: Theory and Algorithms, 2nd Ed., , Wiley-InterscienceMichalewicz, Z., Fogel, D.B., (2000) How to solve It: Modern Heuristics, , SpringerJunqueira, C., De Franca, F., Attux, R., Suyama, R., De Castro, L., Zuben, F.V., Romano, J., A proposal for blind FIR equalization of time-varying channels (2005) Machine Learning for Signal Processing 2005 IEEE Workshop on, pp. 9-14. , SeptJunqueira, C., De Franca, F.O., Attux, R., Panazio, C., De Castro, L., Zuben, F.V., Romano, J., Immune-inspired dynamic optimization for blind spatial equalization in undermodeled channels (2006) Evolutionary Computation, 2006, pp. 2896-2903. , CEC 2006. IEEE Congress onDe Franca, F., Gomes, L., De Castro, L., Zuben, F.V., Handling time-varying TSP instances (2006) Evolutionary Computation, 2006, pp. 2830-2837. , CEC 2006. IEEE Congress onBranke, J., (2001) Evolutionary Optimization in Dynamic Environments, , Norwell, MA, USA: Kluwer Academic PublishersDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251De França, F.O., Von Zuben, F.J., De Castro, L.N., (2005) An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments, " in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), , Washington D.C., USADe Castro, L.N., Timmis, J., An artificial immune network for multimodal optimisation (2002) 2002 Congress on Evolutionary Computation, pp. 699-704. , http://www.cs.ukc.ac.uk/pubs/2002/1374, Part of the 2002 IEEE World Congress on Computational Intelligence. Honolulu, Hawaii, USA: IEEE, May, [Online]. AvailableLi, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.-G., Suganthan, P.N., Benchmark generator for CEC'2009 competition on dynamic optimization (2008) Nanyang Technological University, Tech. Rep., , http://www3.ntu.edu.sg/home/EPNSugan/indexfiles/CEC-09-Dynamic-Opt/ CEC09-Dyn-Opt.htm, Available at, University of Leicester, University of Birmingham, , SeptemberDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach., , SpringerDe Castro, L.N., Von Zuben, F.J., AiNet: An artificial immune network for data analysis (2001) Data Mining: A Heuristic Approach, pp. 231-259. , H. A. Abbass, R. A. Sarker, and C. S. Newton, Eds. Idea Group PublishingBurnet, 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 IncJerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol., Inst. Pasteur, 125 C, pp. 373-389Kiefer, J., Sequential minimax search for a maximum (1995) Proceedings of the American Mathematical Society, 4, pp. 502-50