Abstract
Based on the Antibody Clonal Selection Theory and the dynamic process of immune response, a novel Immune Forgetting Multiobjective Optimization Algorithm (IFMOA) is proposed. IFMOA incorporates a Pareto-strength based antigen-antibody affinity assignment strategy, a clonal selection operation, and a technique simulating the progress of immune tolerance. The comparison of IFMOA with other two representative methods, Multi-objective Genetic Algorithm (MOGA) and Improved Strength Pareto Evolutionary Algorithm (SPEA2), on different test problems suggests that IFMOA extends the searching scope as well as increasing the diversity of the populations, resulting in more uniformly distributing global Pareto optimal solutions and more integrated Pareto fronts over the tradeoff surface.
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
Dasgupta, D., Forrest, S.: Artificial immune systems in industrial applications. In: IPMM 1999. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, pp. 257–267. IEEE press, Los Alamitos (1999)
Abbas, A.K., Lichtman, A.H., Pober, J.S.: Cellular and Molecular Immunology, 3rd edn. W. B. Saunders Company, New York (1998)
Murata, T., Ishibuchi, H., Tanaka, H.: Multi-Objective Genetic Algorithm and Its Application to Flowshop Scheduling. Computers and Industrial Engineering Journal 30(4), 957–968 (1996)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092Zurich, Switzerland (2001)
Li cheng, J., Wang, L.: A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man and Cybernetics, Part A 30(5), 552–561 (2000)
Li cheng, J., Hai feng, D.: Development and Prospect of the Artificial Immune System. Acta Electronica Sinica 31, 73–80 (2003)
Akdis, C.A., Blaser, K., Akdis, M.: Genes of tolerance. Allergy (European Journal of Allergy and Clinical Immunology) 59(9), 897–913 (2004)
Eckart, Z.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. A dissertation submitted to the Swiss Federal Institute of Technology Zurich for the degree of Doctor of Technical Sciences. Diss. Eth No. 13398 (1999)
Schott, J.R.: Fault Tolerant Design Using Single and Multictiteria Gentetic Algorithm Optimization. Master’s thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts (1995)
Van Veldhuizen, D.A., Lamont, G.B.: On measuring multiobjective evolutionary algorithm performance. In: Zalzala, A., Eberhart, R. (eds.) proceeding of the Congress on Evolutionary Computation (CEC 2000), vol. 1, pp. 204–211. IEEE Press, Piscataway (2000)
Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classification, Analyses, and New Innovations. Air Force Institute of Technology (1999), AFIT/DS/ENG/99-01
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, B., Jiao, L., Du, H., Gong, M. (2005). IFMOA: Immune Forgetting Multiobjective Optimization Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_48
Download citation
DOI: https://doi.org/10.1007/11539902_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)