Abstract
The traditional Gravitational Search Algorithm (GSA) has the advantages of easy implementation, fast convergence and low computational cost. However, GSA driven by the gravity law is easy to fall into local optimum solution. The convergence speed slows down in the later search stage, and the solution precision is not good. Inspired by the biological immune system, we introduce the characteristics of antibody diversity and vaccination, and propose a novel immune gravitation optimization algorithm (IGOA) to help speed the convergence of evolutionary algorithms and improve the optimization capability. The comparison experiments of IGOA, GSA and PSO on some benchmark functions are carried out. The proposed algorithm shows competitive results with improved diversity and convergence. It provides new opportunities for solving previously intractable function optimization problems.
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
Rashedi, E.: Gravitational Search Algorithm. MS Thesis, Shahid Bahonar University of Kerman, Iran (2007)
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A Gravitational Search Algorithm. Information Sciences 179(13), 2232–2248 (2009)
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Natural Computing (December 2009), http://dx.doi.org/10.1007/s11047-009-9175-3
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S., et al.: Allocation of Static Var Compensator Using Gravitational Search Algorihm. First Joint Congress on Fuzzy and Intelligent Systems Ferdowsi University of Mashhad, Iran (2007)
Zhan, Z.H., Zhang, J., Li, Y., et al.: Adaptive Particle Swarm Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(6), 1362–1381 (2009)
Chen, D.B., Zhao, C.X.: Particle swarm optimization with adaptive population size and its application. Applied Soft Computing 9(1), 39–48 (2009)
Gu, W.X., Li, X.T., Zhu, L., et al.: A gravitational search algorithm for flow shop scheduling. CAAI Transaction on Intelligent Systems 5(5), 411–418 (2010)
Hoffman, D.: A Brief Overview of the Biological Immune system (2011), http://www.healthy.net/
Dasgupta, D.: Advances in Artificial Immune Systems. IEEE Computational Intelligence Magazine, 40–49 (2006)
Forrest, S., Beauchemin, C.: Computer immunology. Immunological Reviews 216(1), 176–197 (2007)
Zhang, Y., Chen, X.M., Wu, L.H., et al.: MHC-inspired Antibody Clone Algorithm. International Journal of Computational Methods 7(2), 299–318 (2010)
Yao, X., Liu, Y., Lin, G.M.: Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)
Woldesenbet, Y.G., Yen, G.G.: Dynamic Evolutionary Algorithm with Variable Relocation. IEEE Transactions on Evolutionary Computation 13(3), 500–513 (2009)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Wu, L., Zhang, Y., Wang, J. (2011). Immune Gravitation Inspired Optimization Algorithm. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-24728-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24727-9
Online ISBN: 978-3-642-24728-6
eBook Packages: Computer ScienceComputer Science (R0)