Abstract
Shuffled frog leaping Algorithm (SFLA) is a new memetic, local search, population based, Parameter free, meta-heuristic algorithm that has emerged as one of the fast and robust algorithm with efficient global search capability. SFLA has the advantage of social behavior through the process of shuffling and leaping that helps for the infection of ideas. Clonal Selection Algorithms (CSA) are computational paradigms that belong to the computational intelligence family and is inspired by the biological immune system of the human body. CSA has the advantages of Innate and Adaptive Immunity mechanisms to antigenic stimulus that helps the cells to grow its population by the process of cloning whenever required. A hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. This hybrid algorithm performs the parallel computation of social behavior based SFLA and Immune behavior based CSA to improve the ability to reach the global optimal solution with a faster and a rapid convergence rate. This paper compared the Conventional CLONALG and SFLA approaches with the proposed hybrid algorithm and tested on several standard benchmark functions. Experimental results show that the proposed hybrid approach significantly outperforms the existing CLONALG and SFLA approaches in terms of Mean optimal Solution, Success rate, Convergence Speed and Solution stability.
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
Elbeltagi, E., Hegazy, T., Grierson, D.: Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics (19), 43–53 (2005)
Eusuff, M.M., Lansey, K.E., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering Optimization 38(2), 129–154 (2006)
de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Trans. on Evolutionary Computation 6(3), 239–251 (2002)
Castro, D., Timmis, J.: An Introduction to Artificial Immune Systems: A New Computational Intelligence Paradigm. Springer, Heidelberg (2002)
Liong, S.-Y., Atiquzzaman, M.: Optimal design of water distribution network using shuffled complex evolution. J. Inst. Eng. 44(1), 93–107 (2004)
Zhang, X., Hu, X., Cui, G., Wang, Y., Niu, Y.: An Improved Shuffled Frog Leaping Algorithm with Cognitive Behavior. In: Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27 (2008)
Cortes, Coello, C.: Handling Constraints in Global Optimization using an Artificial Immune System
Timmis, J., Edmonds, C., Kelsey, P.: Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimisation. In: Proceedings of the Congress on Evolutionary Computation, pp. 1044–1051 (2004)
Pan, L., Fu, Z.: A Clonal Selection Algorithm for Open Vehicle Routing Problem. In: Proceedings of Third International Conference on Genetic and Evolutionary Computing (2009)
Zuo, X.Q., Fan, Y.S.: A chaos search immune algorithm with its application to neuro-fuzzy controller design. Chaos, Solitons and Fractals 30, 94–109 (2006)
Suganthan, Baskar, S.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Trans. on Evolutionary Computation 10(3) (June 2006)
Ling, S.H., Iu, C.: Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications. IEEE Tran. on Systems, Man and Cybernetics-Part B: Cybernetics 38(3) (June 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chittineni, S., Pradeep, A.N.S., Dinesh, G., Satapathy, S.C., Prasad Reddy, P.V.G.D. (2011). A Parallel Hybridization of Clonal Selection with Shuffled Frog Leaping Algorithm for Solving Global Optimization Problems (P-AISFLA). In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27242-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-27242-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27241-7
Online ISBN: 978-3-642-27242-4
eBook Packages: Computer ScienceComputer Science (R0)