A new parameter free filled function for solving unconstrained global optimization problems

The filled function method is an efficient approach for finding a global minimizer of global optimization problems. This paper introduces a new filled function which overcomes the drawbacks of sensitivity to parameters, containing exponential or logarithmic terms, discontinuity and non-differentiabi...

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Veröffentlicht in:International journal of computer mathematics Jg. ahead-of-print; H. ahead-of-print; S. 1 - 14
1. Verfasser: Ahmed, A. I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.01.2021
Taylor & Francis Ltd
Schlagworte:
Algorithms
filled function
global minimizer
Global optimization
local minimization
Optimization
parameter free
Parameter sensitivity
Unconstrained global optimization
ISSN:0020-7160, 1029-0265
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Zusammenfassung:The filled function method is an efficient approach for finding a global minimizer of global optimization problems. This paper introduces a new filled function which overcomes the drawbacks of sensitivity to parameters, containing exponential or logarithmic terms, discontinuity and non-differentiability for some previous filled functions. It proposes a filled function without any parameters to be adjusted. This filled function has no exponential or logarithmic terms which make the filled function numerically unstable. Also it is continuously differentiable, so gradient information is available in order to use effective local minimization algorithms. Theories of the proposed filled function are investigated and an algorithm for unconstrained global optimization is presented. Numerical results on many test problems with large number of variables are reported. A comparison with some existing algorithms shows that this algorithm is efficient and reliable.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2020.1731484