Neural Computing and Applications

, Volume 23, Issue 7–8, pp 2051–2057 | Cite as

A framework for self-tuning optimization algorithm

  • Xin-She Yang
  • Suash Deb
  • Martin Loomes
  • Mehmet Karamanoglu
Invited Review

Abstract

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.

Keywords

Algorithm Firefly algorithm Parameter tuning Optimization Metaheuristic Nature-inspired algorithm 

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Xin-She Yang
    • 1
  • Suash Deb
    • 2
  • Martin Loomes
    • 1
  • Mehmet Karamanoglu
    • 1
  1. 1.School of Science and TechnologyMiddlesex UniversityLondonUK
  2. 2.Cambridge Institute of TechnologyRanchiIndia

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