Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization

  • Xin-She Yang
  • Suash Deb
Part of the Studies in Computational Intelligence book series (SCI, volume 284)


Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using Lévy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.


Particle Swarm Optimization Local Search Stochastic Optimization Random Search Sequential Quadratic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xin-She Yang
    • 1
  • Suash Deb
    • 2
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK
  2. 2.Department of Computer Science & EngineeringC.V. Raman College of EngineeringBhubaneswarIndia

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