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GSO: An Improved PSO Based on Geese Flight Theory

  • Shengkui Dai
  • Peixian Zhuang
  • Wenjie Xiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

Formation flight of swan geese is one type of swarm intelligence developed through evolution by natural selection. The research on its intrinsic mechanism has great impact on the bionics field. Based on previous research achievements, extensive observation and analysis on such phenomenon, five geese-flight rules and hypotheses are proposed in order to form a concise and simple geese-flight theory framework in this paper. Goose Swarm Optimization algorithm is derived based on the Standard Particle Swam Optimization algorithm. Experimental results show that GSO algorithm is superior in several aspects, such as convergence speed, convergence precision, robustness and etc. The theory offers the in-depth explanations for the performance superiority. Moreover, the rules and hypotheses for formation flight adhere to all five basic principles of swarm intelligence. Therefore, the proposed geese-flight theory is highly rational and has important theoretical innovations, and GSO algorithm can be utilized in a wide range of applications.

Keywords

Geese Theory Goose Swarm Optimization Particle Swarm Optimization Swarm Intelligence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shengkui Dai
    • 1
  • Peixian Zhuang
    • 1
  • Wenjie Xiang
    • 1
  1. 1.College of Information Science and EngineeringNational HuaQiao UniversityChina

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