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Immune Gravitation Inspired Optimization Algorithm

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6838)

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.

Keywords

  • Gravitational search algorithm
  • Optimization
  • Artificial immune system
  • Antibody diversity

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© 2011 Springer-Verlag Berlin Heidelberg

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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

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  • 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)