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Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization

Volume 14, Number 9, September 2018, pp. 2048-2055
DOI: 10.23940/ijpe.18.09.p13.20482055

Weiwei Zhang, Menghua Zhang, Weizheng Zhang, Yinghui Meng, and Huaiguang Wu

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Henan, 450000, China

(Submitted on May 23, 2018; Revised on July 24, 2018; Accepted on August 10, 2018)


Artificial immune systems (AIS) have been widely applied in optimization under static situations. Due to their dynamism, particular challenges are posed when handling dynamic optimization problems (DOPs). The designed algorithms must overcome these challenges to accomplish efficient results. In the paper, a new AIS based algorithm denoted as IAMAIS is proposed. In this algorithm, innate and adaptive responses in the immune system are elaborated on. The innate response is introduced to maintain the diversity of the population and implement global search, while the adaptive immune response is developed to locally locate the optima. Moreover, a memory mechanism is presented to reserve the found optima and further track the optima when environmental change happens. The experiments were applied on the most well-known benchmark, the Moving Peak Benchmark. Simulation results show that IAMAIS is competitive for Dops.


References: 18

                1. T. T. Nguyen, S. Yang, and J. Branke, “Evolutionary Dynamic Optimization: A Survey of the State of the Art,” Swarm Evolutionary Computation, Vol. 6, pp. 1-24, 2012
                2. C. Li, T. T. Nguyen, M. Yang, S. Yang and S. Zeng, “Multi-Population Methods in Unconstrained Continuous Dynamic Environments: The Challenges,” Information Sciences, Vol. 296, pp. 95-118, 2015
                3. D. Dasgupta, S. Yu, and F. Nino, “Recent Advances in Artificial Immune Systems: Models and Applications,” Applied Soft Computing, Vol. 11, No. 2, pp. 1574-1587, 2011
                4. L. N. D. Castro, F. Jose and V. Zuben, “Artificial Immune Systems: Part I-Basic Theory and Applications,” Universidade Estadual de Campinas, Dezembro de, Technical Report 210.1, 1999
                5. L. N. de Castro and F. J. Von Zuben, “Learning and Optimization using the Clonal Selection Principle,” IEEE Transactions on Evolutionary Computation, Vol. 6, pp. 239-251, 2002
                6. K. Trojanowski and S. T. Wierzchon, “Immune-Based Algorithms for Dynamic Optimization,” Information Sciences, Vol. 179, pp. 1495-1515, 2009
                7. A. Gaspar and P. Collard, “From GAs to Artificial Immune Systems: Improving Adaptation in Time Dependent Optimization,” in Proceedings of the Congress on Evolutionary Computation, IEEE Press, Vol. 3, pp. 1859-1866, Piscataway, New Jersey, 1999
                8. F. O. D. Franca, F. J. V. Zuben, and L. N. D. Castro, “An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments,” in Proceedings of Genetic and Evolutionary Computation Conference, pp. 289-296, 2005
                9. F. O. D. Franca and F. J. V. Zuben, “A Dynamic Artificial Immune Algorithm Applied to Challenging Benchmarking Problems,” in Proceedings of IEEE Congress on Evolutionary Computation, pp. 423-430, 2009
                10. S. Qian, “Dynamic Stochastic Ranking Selection Immune Optimization Algorithm for Dynamical 0/1 Knapsack Problem,” in Proceedings of International Conference on Intelligent Human-Machine Systems and Cybernetics Dynamic, pp. 100-103, 2013
                11. V. S. Aragon, S. C. Esquivel and C. A. C. Coello, “A T-Cell Algorithm for Solving Dynamic Optimization Problems,” Information Sciences, Vol. 181, pp. 3614-3637, 2011
                12. V. S. Aragon, S. C. Esquivel and C. A. Coello, “Artificial Immune System for Solving Dynamic Constrained Optimization Problems,” Metaheuristics for Dynamic Optimization, Vol. 35, pp. 225-263, 2013
                13. A. Sharifi, J. K. Kordestani, M. Mahdaviani, and M. R. Meybodi, “A Novel Hybrid Adaptive Collaborative Approach based on Particle Swarm Optimization and Local Search for Dynamic Optimization Problems,” Applied Soft Computing, Vol.32, pp. 432-448, 2015
                14. J. Branke,“Evolutionary Optimization in Dynamic Environments,” Kluwer Academic Publishers, Boston, MA, 2002
                15. T. Blackwell and J. Branke, “Multiswarms, Exclusion, and Anti-Convergence in Dynamic Environments,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 459-472, 2006
                16. A. B. Hashemi and M. R. Meybodi, “Cellular Pso: A PSO for Dynamic Environments,” Advances in Computation and Intelligence, pp. 422-433, 2009
                17. W. Du and B. Li, “Multi-Strategy Ensemble Particle Swarm Optimization for Dynamic Optimization,” Information Sciences, Vol. 178, pp. 3096-3109, 2008
                18. R. I. Lung and D. Dumitrescu, “A Collaborative Model for Tracking Optima in Dynamic Environments,” in Proceedings of IEEE Congress on Evolutionary Computation, pp. 564-567, 2007


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