Enhanced kernel search algorithm for optimizing local search capability and its application to carbon fiber draft process

Kernel Search Optimization (KSO) is characterized by insufficient accuracy in local search, which makes it difficult to achieve local optimization. Therefore, this paper proposes a Large Local Search Kernel Search Optimization (LLSKSO) to enhance the local optimization ability. LLSKSO achieves the p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one Jg. 20; H. 11; S. e0334348
Hauptverfasser: Dong, Ruyi, Cui, Ran, Cai, Zhennao, Heidari, Ali Asghar, Liu, Lei, Liu, Yanan, Chen, Huiling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 26.11.2025
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Animals
Beetles
Benchmarks
Carbon
Carbon Fiber
Carbon fibers
Coleoptera
Drafting software
Dung
Innovations
Local optimization
Mathematical optimization
Models, Theoretical
Optimization algorithms
Search algorithms
ISSN:1932-6203, 1932-6203
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Kernel Search Optimization (KSO) is characterized by insufficient accuracy in local search, which makes it difficult to achieve local optimization. Therefore, this paper proposes a Large Local Search Kernel Search Optimization (LLSKSO) to enhance the local optimization ability. LLSKSO achieves the performance improvement by introducing several strategies. First, the initial population is homogenized using the good point set mechanism. Then, the little dung beetle search mechanism of the Dung Beetle Optimizer (DBO) is introduced to enhance the local search capability of the KSO. Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. From the statistical results of mean, best and variance of different algorithms, the LLSKSO algorithm outperforms the other algorithms. Finally, LLSKSO is applied to the engineering problem of carbon fiber drafting ratio optimization. Moreover, the experimental results obtained by LLSKSO yielded smaller line densities and greater strengths compared to other algorithms. LLSKSO achieves theoretical optima in 16 out of 20 high-dimensional benchmark functions, with an average CPU runtime reduced by 30% compared to baseline methods. Therefore, it can be shown that LLSKSO can be used as an effective optimization algorithm and engineering assistance.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0334348