An efficient weighted slime mould algorithm for engineering optimization DOI Creative Commons
Qibo Sun, Chaofan Wang, Yi Chen

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 4, 2024

Language: Английский

Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets DOI Creative Commons
Wei Zhu,

Yuxi Hu,

Lei Liu

et al.

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 28, 2025

Language: Английский

Citations

0
Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction DOI

Jungang Zhao,

J. Li,

Jiangqiao Yao

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 175, P. 108437 - 108437

Published: April 9, 2024

Language: Английский

Citations

3
An advanced kernel search optimization for dynamic economic emission dispatch with new energy sources DOI Creative Commons
Ruyi Dong,

Lixun Sun,

Zhennao Cai

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2024, Volume and Issue: 160, P. 110085 - 110085

Published: June 27, 2024

Language: Английский

Citations

3
Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model DOI

Rizeng Li,

Sunmeng Chen,

Jianfu Xia

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 175, P. 108447 - 108447

Published: April 12, 2024

Language: Английский

Citations

2
Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease DOI

Jinlei Mao,

Zhihao Zhu,

Minjun Xia

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 175, P. 108394 - 108394

Published: April 16, 2024

Language: Английский

Citations

2
Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study DOI
Xuting Chen, Dongying Zhao, Haochen Ji

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 174, P. 108439 - 108439

Published: April 16, 2024

Language: Английский

Citations

2
Advances in Slime Mould Algorithm: A Comprehensive Survey DOI Creative Commons

Yuanfei Wei,

Zalinda Othman, Kauthar Mohd Daud

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(1), P. 31 - 31

Published: Jan. 4, 2024

The slime mould algorithm (SMA) is a new swarm intelligence inspired by the oscillatory behavior of moulds during foraging. Numerous researchers have widely applied SMA and its variants in various domains field proved value conducting literatures. In this paper, comprehensive review introduced, which based on 130 articles obtained from Google Scholar between 2022 2023. study, firstly, theory described. Secondly, improved are provided categorized according to approach used apply them. Finally, we also discuss main applications SMA, such as engineering optimization, energy machine learning, network, scheduling image segmentation. This presents some research suggestions for interested algorithm, additional multi-objective discrete SMAs extending neural networks extreme learning machining.

Language: Английский

Citations

1
Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data DOI Creative Commons
Feng Qiu, Ali Asghar Heidari, Yi Chen

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 13, 2024

Abstract Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is great significance for clinical decision-making. Gene selection (GS) an important preprocessing technique that aims to select a subset feature information improve performance reduce dimensionality. This study proposes improved wrapper GS method based on forensic-based investigation (FBI). The introduces the search mechanism slime mould algorithm in FBI original FBI; newly proposed named SMA_FBI; then performed by converting continuous optimizer binary version transfer function. In order verify superiority SMA_FBI, experiments are first executed 30-function test set CEC2017 compared with 10 algorithms state-of-the-art algorithms. experimental results show SMA_FBI better than other terms finding optimal solution, convergence speed, robustness. addition, BSMA_FBI (binary SMA_FBI) 8 18 high-dimensional from UCI repository. indicate able obtain classification accuracy fewer features selected applications. Therefore, considered optimization tool potential dealing global problems, its version, BSMA_FBI, can be used tasks.

Language: Английский

Citations

1
Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images DOI
Jinge Shi, Yi Chen, Chaofan Wang

et al.

Displays, Journal Year: 2024, Volume and Issue: 84, P. 102799 - 102799

Published: July 20, 2024

Language: Английский

Citations

1
Multi-threshold image segmentation using a boosted whale optimization: case study of breast invasive ductal carcinomas DOI
Jinge Shi, Yi Chen, Zhennao Cai

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14891 - 14949

Published: Aug. 7, 2024

Language: Английский

Citations

1