Abstract
In this paper, an improved model of the immune system to handle constraints in multi-criteria optimization problems has been proposed. The problem that is of interest to us is the new task assignment problem for a distributed computer system. Both a workload of a bottleneck computer and the cost of machines are minimized; in contrast, a reliability of the system is maximized. Moreover, constraints related to memory limits, task assignment and computer locations are imposed on the feasible task assignment. Finally, an evolutionary algorithm based on tabu search procedure and the immune system model is proposed to provide task assignments.
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References
Balicki, J., Kitowski, Z.: Tabu-based evolutionary algorithm for effective program module assignment in parallel processing. WSEAS Transactions on Systems 3, 119–124 (2004)
Coello Coello, C.A., Cortes, N.C.: Use of Emulations of the Immune System to Handle Constraints in Evolutionary Algorithms, Knowledge and Information Systems. An International Journal 1, 1–12 (2001)
Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
D’haeseleer, P., et al.: An Immunological Approach to Change Detection. In: Proc. of IEEE Symposium on Research in Security and Privacy, Oakland (1996)
Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System, Adaptation, and Machine Learning. Physica D 22, 187–204 (1986)
Forrest, S., Perelson, A.S.: Genetic Algorithms and the Immune System. LNCS, pp. 320–325. Springer, Heidelberg (1991)
Helman, P., Forrest, S.: An Efficient Algorithm for Generating Random Antibody Strings. Technical Report CS-94-07, The University of New Mexico, Albuquerque (1994)
Jerne, N.K.: The Immune System. Scientific American 229(1), 52–60 (1973)
Kim, J., Bentley, P.J.: Immune Memory in the Dynamic Clonal Selection Algorithm. In: Proc. of the First Int. Conf. on Artificial Immune Systems, Canterbury, pp. 57–65 (2002)
Koziel, S., Michalewicz, Z.: Evolutionary Algorithms, Homomorphous mapping, and Constrained Parameter Optimisation. Evolutionary Computation 7, 19–44 (1999)
Smith, D.: Towards a Model of Associative Recall in Immunological Memory. Technical Report 94-9, University of New Mexico, Albuquerque (1994)
Weglarz, J. (ed.): Recent Advances in Project Scheduling. Kluwer Academic Publishers, Dordrecht (1998)
Wierzchon, S.T.: Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System. In: Klopotek, M., Michalewicz, M., Wierzchon, S.T. (eds.) Intelligent Information Systems, pp. 119–133. Springer, Heidelberg (2000)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)
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Balicki, J. (2005). Immune Systems in Multi-criterion Evolutionary Algorithm for Task Assignments in Distributed Computer System. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds) Advances in Web Intelligence. AWIC 2005. Lecture Notes in Computer Science(), vol 3528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11495772_9
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DOI: https://doi.org/10.1007/11495772_9
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