Abstract
This paper presents an novel evolution and immune hybrid algorithm for multimodal function optimization. The algorithm constructs a multi-dimensional shape-space based on immune theory and approaches optima by steepest descent evolution strategy along each dimension, adjusts steps adaptively based on fitness in each iteration, as a result, gets steepest and surefooted ability approaching the optima. By suppressing close individuals in immune shape-space within a restraint radius and supplying new individuals to exploit new searching space, the algorithm obtains very good diversity. Experiments for multimodal functions show that the algorithm achieved global searching effect, obtained all the optima in shorter iterations and with lesser size of population compared with the GA, CSA and op-aiNet.
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Beyer, H.-G., Schwefel, H.-P.: Evolution strategies. Natural Computing 1, 3–52 (2002)
Fukuda, T., Mcri, K., Tsukiama, 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., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications. In: Workshop Proceedings of GECCO, USA. Artificial Immune Systems and Their Applications, vol. 7, pp. 36–37 (2000)
de Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: part I - Basic theory and applications. Technical Report, vol. 12 (1999)
De Castro, F.J.: An Evolution Immune Network for Data Clustering. Proc. Of the IEEE 11, 84–89 (2000)
de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: IEEE 2002, pp. 699–704. IEEE Computer Society Press, Los Alamitos (2002)
Tang, T., Qiu, J.: An Improved Multimodal Artificial Immune Algorithm and its Convergence Analysis. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, vol. 6, pp. 3335–3339. IEEE, Los Alamitos (2006)
Coello Coello, C.A.: Solving Multiobjective Optimization Problems Using an Artificial Immune System. Genetic Programming and Evolvable Machines 6, 163–190 (2005)
Power, D., Ryan, C., Azad, R.M.A.: Promoting diversity using migration strategies in distributed genetic algorithms. IEEE Transaction on Evolutionary Computation 5, 1831–1838 (2005)
Im, C.-H., Kim, H.-K., Jung, H.-K., Choi, K.: A novel algorithm for multimodal function optimization based on evolution strategy. IEEE Transaction Magnetics 40(2), 1224–1227 (2004)
Yao, J., Kharma, N., Grogono, P.: BMPGA: A Bi-Objective Multi-population Genetic Algorithm for Multi-modal Function Optimization. In: IEEE2005, pp. 816–823. IEEE, Los Alamitos (2005)
Singh, G., Deb, K.: Comparison of Multi-Modal Optimization Algorithms Based on Evolutionary Algorithms. In: GECCO 2006. Proceedings of the 8th annual conference on Genetic and evolutionary computation, Washington, USA, vol. 7, pp. 1305–1312 (2006)
Corns, S.M., Ashlock, D.A., McCorkle, D.S., Bryden, K.M.: Improving Design Diversity Using Graph Based Evolutionary Algorithms. In: Congress on Evolutionary Computation, Vancouver, BC, Canada, vol. 7, pp. 333–339. IEEE, Los Alamitos (2006)
Yoshiaki, S., Noriyuki, T., Yukinobu, H., Katsuari, K.: A Fuzzy Clustering Based Selection Method to Maintain Diversity in Genetic Algorithms. In: Congress on Evolutionary Computation, Vancouver, BC, Canada, vol. 7, pp. 3007–3012. IEEE, Los Alamitos (2006)
Narihisa, H., Kohmoto, K., Taniguchi, T., Ohta, M., Katayama, K.: Evolutionary Programming With Only Using Exponential Mutation. In: Congress on Evolutionary Computations, Vancouver, BC, Canada, vol. 7, IEEE, Los Alamitos (2006)
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Zhu, L., Li, Z., Sun, B. (2007). A Steepest Descent Evolution Immune Algorithm for Multimodal Function Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_44
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DOI: https://doi.org/10.1007/978-3-540-74581-5_44
Publisher Name: Springer, Berlin, Heidelberg
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