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Neural Computing and Applications
Published in: Neural Computing and Applications 7-8/2013

01-12-2013 | Invited Review

A framework for self-tuning optimization algorithm

Authors: Xin-She Yang, Suash Deb, Martin Loomes, Mehmet Karamanoglu

Published in: Neural Computing and Applications | Issue 7-8/2013

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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.
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Metadata
Title
A framework for self-tuning optimization algorithm
Authors
Xin-She Yang
Suash Deb
Martin Loomes
Mehmet Karamanoglu
Publication date
01-12-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7-8/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1498-4

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