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Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization

Dalirinia, Elham; Jalali, Mehrdad ORCID iD icon 1; Yaghoobi, Mahdi; Tabatabaee, Hamid
1 Institut für Funktionelle Grenzflächen (IFG), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy.


Verlagsausgabe §
DOI: 10.5445/IR/1000160203
Veröffentlicht am 06.07.2023
Originalveröffentlichung
DOI: 10.1007/s11227-023-05513-8
Scopus
Zitationen: 12
Web of Science
Zitationen: 8
Dimensions
Zitationen: 13
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 0920-8542, 1573-0484
KITopen-ID: 1000160203
HGF-Programm 43.33.11 (POF IV, LK 01) Adaptive and Bioinstructive Materials Systems
Erschienen in The Journal of Supercomputing
Verlag Springer
Band 80
Seiten 761–799
Vorab online veröffentlicht am 03.07.2023
Nachgewiesen in Scopus
Web of Science
Dimensions
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