Pareto-based self-organizing migrating algorithm
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244229" target="_blank" >RIV/61989100:27240/19:10244229 - isvavai.cz</a>
Výsledek na webu
<a href="https://mendel-journal.org/index.php/mendel/article/view/87" target="_blank" >https://mendel-journal.org/index.php/mendel/article/view/87</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/mendel.2019.1.111" target="_blank" >10.13164/mendel.2019.1.111</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Pareto-based self-organizing migrating algorithm
Popis výsledku v původním jazyce
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. (C) 2019, Brno University of Technology. All rights reserved.
Název v anglickém jazyce
Pareto-based self-organizing migrating algorithm
Popis výsledku anglicky
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. (C) 2019, Brno University of Technology. All rights reserved.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2019
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Mendel. Volume 25
ISBN
—
ISSN
1803-3814
e-ISSN
2571-3701
Počet stran výsledku
10
Strana od-do
111-120
Název nakladatele
Brno University of Technology
Místo vydání
Brno
Místo konání akce
Brno
Datum konání akce
10. 7. 2019
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—