A New Integral Function Algorithm for Global Optimization and Its Application to the Data Clustering Problem
Loading...
Date
2023-12-31
Authors
Pandiya, Ridwan
Ahdika, Atina
Khomsah, Siti
Ramadhani, Rima Dias
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Automation and Computer Science, Brno University of Technology
Altmetrics
Abstract
The filled function method is an approach to finding global minimum points of multidimensional unconstrained global optimization problems. The conventional parametric filled functions have computational weaknesses when they are employed in some benchmark optimization functions. This paper proposes a new integral function algorithm based on the auxiliary function approach. The proposed method can successfully be used to find the global minimum point of a function of several variables. Some testing global optimization problems have been used to show the ability of this recommended method. The integral function algorithm is then implemented to solve the center-based data clustering problem. The results show that the proposed algorithm can solve the problem successfully.
Description
Citation
Mendel. 2023 vol. 29, č. 2, s. 162-168. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/251
https://mendel-journal.org/index.php/mendel/article/view/251
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0
http://creativecommons.org/licenses/by-nc-sa/4.0