Recommendation Model for Youth Sports Nutrition Programs Based on Multi-Population Cooperative IPSO Algorithm DOI

Chunping Tan,

Mei Yang,

Yiping Lu

et al.

Published: Dec. 13, 2024

Language: Английский

Sub-population evolutionary particle swarm optimization with dynamic fitness-distance balance and elite reverse learning for engineering design problems DOI
Gang Hu,

Keke Song,

Mahmoud Abdel-Salam

et al.

Advances in Engineering Software, Journal Year: 2025, Volume and Issue: 202, P. 103866 - 103866

Published: Jan. 30, 2025

Language: Английский

Citations

4
REBSA: Enhanced backtracking search for multi-threshold segmentation of breast cancer images DOI

Shiqi Xu,

Wei Jiang, Yi Chen

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107733 - 107733

Published: March 11, 2025

Language: Английский

Citations

0
GPSOM: group-based particle swarm optimization with multiple strategies for engineering applications DOI Creative Commons

Jialing Yan,

Gang Hu,

Heming Jia

et al.

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

Published: May 9, 2025

Language: Английский

Citations

0
A Novel Approach for Predicting CO2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model DOI Creative Commons

Yuyi Hu,

Bojun Wang, Yanping Yang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4379 - 4379

Published: Sept. 1, 2024

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model predicting CO2 that combines multi-strategy improved particle swarm optimization (MSPSO) algorithm with long short-term memory (LSTM) model. Firstly, (PSO) is enhanced by combining tent chaotic mapping, mutation least-fit particles, random perturbation strategy. Subsequently, performance MSPSO evaluated using set 23 internationally recognized test functions. Finally, predictive MSPSO-LSTM assessed from Yangtze River Delta region as case study. results indicate coefficient determination (R2) reaches 0.9677, which more than 10% higher BP, LSTM, CNN non-hybrid models demonstrates significant advantages over PSO-LSTM, GWO-LSTM, WOA-LSTM models. Additionally, mean square error (MSE) 2445.6866 Mt, absolute (MAE) 4.1010 both significantly lower those Overall, high accuracy industry, offering robust development industry.

Language: Английский

Citations

2
Recommendation Model for Youth Sports Nutrition Programs Based on Multi-Population Cooperative IPSO Algorithm DOI

Chunping Tan,

Mei Yang,

Yiping Lu

et al.

Published: Dec. 13, 2024

Language: Английский

Citations

0