Summary
The mammal immune system is a distributed multiagent system. Its properties of distributive control and self organization have created interest in using immune principles to solve complex engineering tasks such as decentralized robot control, pattern recognition, multimodal and combinatorial optimization. In this paper a new immunity-based algorithm for solving optimization problems is proposed. The algorithm differs from the representative immune algorithm CLONALG. The agents participating in distributed problem solving enrich their knowledge about the solution via communication with other agents. Moreover they are decomposed into groups of specialists that can modify only some decision variables and/or use their own method of local improvement of the solution. The empirical results confirming usability of the algorithm and its advantage over CLONALG are presented. Obtained estimates of the global optima of multimodal test functions and traveling salesperson problem (TSP) are closer to the theoretical solutions and require fewer tentative computations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. John Willey & Sons (1989)
De Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 3, 239–251 (2002)
Carter, J.H.: The Immune System as a Model for Pattern Recognition and Classification. Journal of the American Medical Informatics Association 1, 28–41 (2000)
Gutknecht, O., Ferber, J., Michel, F.: The MadKit Agent Platform Architecture. Rapport De Recherche, LIRM, Universite Montpellier, France (2000)
Jerne, N.K.: The Immune System. Scientific American 229(1), 52–60 (1973)
Lau, H.Y.K., Wong, V.W.K.: An Immunity-Based Distributed Multiagent-Control Framework. IEEE Transactions on Systems, Man, and Cybernetics - part A: Systems and Humans 1, 91–108 (2006)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolutionary Programs. Springer, Heidelberg (1996)
Sathyanath, S., Sahin, F.: AISIMAM An Artificial Immune System Based Intelligent Multi Agent Model and its Application to a Mine Detection Problem. In: Proc. ICARIS (2002)
Villalobos-Arias, M., Coello Cello, C.A., Hernandez Lerma, O.: Convergence Analysis of Multiobjective Artificial Immune Algorithm. Proc. ICARIS, 226–235 (2004)
Wierzchon, S.T.: Artificial Immune Systems. Theory and Applications (in Polish). AOW EXIT, Warszawa (2001)
Wierzchon, S.T.: Multimodal optimization with artificial immune system. In: Klopotek, M.A., Michalewicz, Z., Wierzchon, S.T. (eds.) Intelligent Information Systems. Physica-Verlag (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oszust, M., Wysocki, M. (2008). A Distributed Immune Algorithm for Solving Optimization Problems. In: Badica, C., Mangioni, G., Carchiolo, V., Burdescu, D.D. (eds) Intelligent Distributed Computing, Systems and Applications. Studies in Computational Intelligence, vol 162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85257-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-85257-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85256-8
Online ISBN: 978-3-540-85257-5
eBook Packages: EngineeringEngineering (R0)