Abstract
This work presents omni-aiNet, an immune-inspired algorithm developed to solve single and multi-objective optimization problems, either with single and multi-global solutions. The search engine is capable of automatically adapting the exploration of the search space according to the intrinsic demand of the optimization problem. This proposal unites the concepts of omni-optimization, already proposed in the literature, with distinctive procedures associated with immune-inspired concepts. Due to the immune inspiration, the omni-aiNet presents a population capable of adjusting its size during the execution of the algorithm, according to a predefined suppression threshold, and a new grid mechanism to control the spread of solutions in the objective space. The omni-aiNet was applied to several optimization problems and the obtained results are presented and analyzed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms, 2nd edn. John-Wiley & Sons, West Sussex (1993)
Burnet, F.M.: Clonal Selection and After. In: Bell, G.I., Perelson, A.S., Pimgley Jr., G.H. (eds.) Theoretical Immunology, pp. 63–85. Marcel Dekker Inc., New York (1978)
Coello Coello, C.A., Cortés, N.C.: Solving Multiobjective Optimization Problems Using an Artificial Immune System. Genetic Programming and Evolvable Machines 6, 163–190 (2005)
de Castro, L.N., Timmis, J.: An Introduction to Artificial Immune Systems: A New Computational Intelligence Paradigm. Springer, Heidelberg (2002)
de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: Proc. IEEE CEC, USA, pp. 669–674 (2002)
de Castro, L.N., Von Zuben, F.J.: aiNet: An Artificial Immune Network for Data Analysis. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, ch. XII, pp. 231–259. Idea Group Publishing, USA (2001)
de França, F.O., Von Zuben, F.J., de Castro, L.N.: An Artificial Immune Network for Multimodal Funcion Optimization on Dynamic Environments. In: Proc. GECCO, Washington, DC, USA, pp. 289–296 (2005)
Deb, K., Tiwari, S.: Omni-optimizer: A Procedure for Single and Multi-objective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 47–61. Springer, Heidelberg (2005)
Gomes, L.C.T., de Sousa, J.S., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J.: Copt-aiNet and the Gene Ordering Problem. Information Technology Magazine, Catholic University of Brasília 3(2), 27–33 (2003)
Holland, P.W.H., Garcia-Fernandez, J., Williams, N.A., Sidow, A.: Gene Duplications and Origins of the Vertebrate Development. Dev. Supp., 125–133 (1994)
Jerne, N.K.: Towards a Network Theory of the Immune System. Ann. Immunol., Inst. Pasteur 125C, 373–389 (1974)
Ohno, S.: Evolution by Gene Duplication. Allen and Unwin, London (1970)
Zitzler, E., Thiele, L., Deb, K.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evol. Computation 8(2), 173–195 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Coelho, G.P., Von Zuben, F.J. (2006). omni-aiNet: An Immune-Inspired Approach for Omni Optimization. In: Bersini, H., Carneiro, J. (eds) Artificial Immune Systems. ICARIS 2006. Lecture Notes in Computer Science, vol 4163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823940_23
Download citation
DOI: https://doi.org/10.1007/11823940_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37749-8
Online ISBN: 978-3-540-37751-1
eBook Packages: Computer ScienceComputer Science (R0)