An Artificial Immune Network For Multimodal Function Optimization On Dynamic Environments

Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature. Copyright 2005 ACM.289296Allen, D., (1987) Timing, Genetic Requirements and Functional Consequences of Somatic Hypermutation during B-cell Development, Imm. Rev., 96, pp. 5-22Angeline, P.J., Evolutionary optimization versus particle swarm optimizationPhilosophy and performance differences (1998) Proc. Evol. Prog. VII, pp. 601-610. , V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Berlin, Germany: Springer-VerlagAngeline, P.J., Tracking extrema in dynamic environments (1997) Lecture Notes in Computer Science, 1213. , Proceedings of the 6th International Conference on Evolutionary Programming (Indianapolis,Indiana, USA, Apr, 13-16), P. J. Angeline, R. G. Reynolds, J. R. McDonnell, and R. C. Eberhart, Eds.,Springer VerlagBazaraa, M.S., Sherali, H.D., Shetty, C.M., (1993) Nonlinear Programming: Theory and Algorithms, 2nd Ed., , WileyDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6 (3), pp. 239-251De Castro, L.N., Timmis, J., Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagDe Castro, L.N., Timmis, J., An artificial immune network for multimodal function optimization (2002) Proceedings of the IEEE Congress on Evolutionary Computation (CEC'02), 1, pp. 699-1674. , May, HawaiiGaspar, A., Collard, P., From GAs to artificial immune systems: Improving adaptation in time dependent optimization Proceedings of the Congress on Evolutionary Computation, pp. 1859-1866. , Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzal (Eds)Holland, P.W.H., Garcia-Femandez, J., Williams, N.A., Sidow, A., Gene duplications and origins of vertebrate development (1994) Development Supplement, pp. 125-133Kelsey, J., Timmis, J., Hone, A., Chasing chaos Proceedings of the Congress on Evolutionary Computation, pp. 413-419. , R. Sarker, R. Reynolds, H. Abbass, T. Kay-Chen, R. McKay, D. Essam, and T. Gedeon, editors, Canberra. Australia, December. IEEELeung, Y., Wang, Y., An orthogonal genetic algorithm with quantization for global numerical optimization (2001) IEEE Trans. Evol. Comput., 5 (1), pp. 41-53Ohno, S., (1970) Evolution by Gene Duplication, , Allen and Unwin, LondonPerelson, A.S., (1989) Immune Network Theory, Imm. Rev., 110, pp. 5-36Siarry, P., Berthiau, G., Durbin, F., Haussy, J., Enhanced simulated annealing for globally minimizing functions of many-continuous variables (1990) Neural Networks, 3, pp. 467-483Timmis, J., Edmonds, C., A comment on opt-AINet: An immune network algorithm for optimisation (2004) Lecture Notes in Computer Science, 3102, pp. 308-317. , D. Kalyanmoy et al, editor, Genetic and Evolutionary Computation, SpringerTimmis, J., Edmonds, C., Kelsey, J., Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation (2004) Proceedings of the Congress on Evolutionary Computation, 1, pp. 1044-1051Walker, J., Garrett, S., Dynamic function optimisation: Comparing the performance of clonal selection and evolutionary strategies Lecture Notes in Computer Science, 2787, pp. 273-284. , Timmis, J., Bentley, P. and Hart, E.(Eds)Yao, X., Liu, Y., Fast evolution strategies (1997) Evolutionary Programming VI, pp. 151-161. , P. J. Angeline, R. Reynolds, J. McDonnell, and R. Eberhart, Eds. Berlin, Germany: Springer-Verla