Self-Organizing Migrating Algorithm Pareto
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Date
2019-06-24
Authors
Diep, Quoc Bao
Zelinka, Ivan
Das, Swagatam
ORCID
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Referee
Mark
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Volume Title
Publisher
Institute of Automation and Computer Science, Brno University of Technology
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Abstract
In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC'13, CEC'15, and CEC'17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.
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Citation
Mendel. 2018 vol. 25, č. 1, s. 111-120. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/87
https://mendel-journal.org/index.php/mendel/article/view/87
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Peer-reviewed
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en
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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