Abstract
This paper proposes a Clonal Selection Algorithm for Multimodal function optimization (CSAM) based on the concepts of artificial immune system and antibody clonal selection theory. In CSAM, more attention is paid to locate all the peaks (both global and local ones) of multimodal optimization problems. To achieve this purpose, new clonal selection operator is put forward, dynamic population size and clustering radius are also used not only to locate all the peaks as many as possible, but assure no resource wasting, i.e., only one antibody will locate in each peak. Finally, new performances are also presented for multimodal function when there is no prior idea about it in advance. Our experiments demonstrated that CSAM is very effective in dealing with multimodal optimization regardless of global or local peaks.
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
De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Thesis, University of Michigan, Ann Arbor, MI. Dissertation Abstracts International 36(10), 5410B (University Microfilms No. 76-9381)
Goldberg, D.E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization. In: Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, pp. 41–49 (1987)
Fukuda, T., Mori, K., Tsukiyama, M.: Parallel Search for Multi-Modal Function Optimization with Diversity and Learning of Immune Algorithm. In: Dasgupta, D. (ed.) Artificial Immune Systems and Their Applications, pp. 210–220. Springer, Heidelberg (1999)
de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: Eberhart, R. (ed.) Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, Hawaii, May 12-17, 2002, pp. 699–704. IEEE Service Center, Los Alamitos (2002)
Burton Havrvey, K., Pettey, C.C.: The Outlaw Method for Solving Multimodal Function with Split Ring Parallel Genetic Algorithms. In: Proceedings of GECCO 1999 (the Genetic and Evolutionary Computation Conference), Orlando, Florida, July 13-17, 1999, pp. 274–288. Morgan Kaufmann Publishers, San Francisco (1999)
Chang-Hwan, I., Hong-Kyu, K., Hyun-Kyo, J., Choi, K.: A Novel Algorithm for Multimodal Function Optimization Based on Evolution Strategy. IEEE Trans. on magnetics 40(2), 1224–1227 (2004)
Higashi, N., Iba, H.: Particle Swarm Optimization with Gaussian Mutation. In: IEEE Swarm Intelligence Symposium, pp. 72–79 (2003)
Susana, C.E., Carlos, A.C.C.: On the Use of Particle Swarm Optimization with Multimodal Functions. In: Proceedings of IEEE Congress on Evolutionary Computation 2003 (CEC 2003), Canbella, Australia, pp. 1130–1136 (2003)
De Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Trans. On Evol. Comp., Special Issue on Artificial Immune System 6(3), 239–251 (2001)
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
Hong-yun, M., Xiao-hua, Z., San-yang, L. (2006). A Novel Clonal Selection for Multi-modal Function Optimization. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_9
Download citation
DOI: https://doi.org/10.1007/11881223_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
Online ISBN: 978-3-540-45909-5
eBook Packages: Computer ScienceComputer Science (R0)