Griffon vultures optimization algorithm for solving optimization problems DOI
Dler O. Hasan, Hardi M. Mohammed, Zrar Kh. Abdul

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127206 - 127206

Published: March 1, 2025

Language: Английский

Dream Optimization Algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems DOI

Yidong Lang,

Yuelin Gao

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 436, P. 117718 - 117718

Published: Jan. 9, 2025

Language: Английский

Citations

4
A Novel Ensemble Learning Framework Based on News Sentiment Enhancement and Multi-objective Optimizer for Carbon Price Forecasting DOI
Yujie Chen, Mingyao Jin, Zheyu Zhou

et al.

Computational Economics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

Citations

3
Enhancing accuracy in point-interval load forecasting: A new strategy based on data augmentation, customized deep learning, and weighted linear error correction DOI
Weican Liu, Zhirui Tian, Yuyan Qiu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126686 - 126686

Published: Feb. 1, 2025

Language: Английский

Citations

2
Weighted average algorithm: a novel meta-heuristic optimization algorithm based on the weighted average position concept DOI
Jun Cheng, Wim De Waele

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: unknown, P. 112564 - 112564

Published: Oct. 1, 2024

Language: Английский

Citations

15
Improved multi-strategy beluga whale optimization algorithm: a case study for multiple engineering optimization problems DOI
Hao Zou, Kai Wang

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

Citations

1
An enhanced ivy algorithm fusing multiple strategies for global optimization problems DOI

Chunqiang Zhang,

Wenzhou Lin, Gang Hu

et al.

Advances in Engineering Software, Journal Year: 2025, Volume and Issue: 203, P. 103862 - 103862

Published: Feb. 6, 2025

Language: Английский

Citations

1
Improved snow geese algorithm for engineering applications and clustering optimization DOI Creative Commons
Haihong Bian, Can Li, Yuhan Liu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 6, 2025

The Snow Goose Algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that easy fall into local optimal and premature convergence. In order further improve the performance of algorithm, this paper proposes an improved (ISGA) based on three strategies according real migration habits snow geese: (1) Lead goose rotation mechanism. (2) Honk-guiding (3) Outlier boundary strategy. Through above strategies, exploration development ability original comprehensively enhanced, convergence accuracy speed improved. paper, two standard test sets IEEE CEC2022 CEC2017 used verify excellent algorithm. practical application ISGA tested through 8 engineering problems, employed enhance effect clustering results show compared with comparison faster iteration can find better solutions, shows its great potential solving problems.

Language: Английский

Citations

1
Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training DOI Creative Commons
Rui Zhong, Chao Zhang, Jun Yu

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 110, P. 77 - 98

Published: Oct. 7, 2024

Language: Английский

Citations

8
A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism DOI
Zhaochen Dong, Zhirui Tian,

Shuang Lv

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 162, P. 111863 - 111863

Published: June 15, 2024

Language: Английский

Citations

7
Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm DOI Creative Commons
Xing Wang, Yikun Su,

Zhizhe Zheng

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e35595 - e35595

Published: Aug. 1, 2024

Providing accurate prediction of the severity traffic collisions is vital to improve efficiency emergencies and reduce casualties, accordingly improving safety reducing congestion. However, issue both predictive accuracy model interpretability predicted outcomes has remained a persistent challenge. We propose Random Forest optimized by Meta-heuristic algorithm framework that integrates spatiotemporal characteristics crashes. Through analysis motor vehicle crash data on interstate highways within United States in 2020, we compared various ensemble models single-classification models. The results show (RF) Crown Porcupine Optimizer (CPO) best results, accuracy, recall, f1 score, precision can reach more than 90 %. found factors such as Temperature Weather are closely related Closely indicators were analyzed interpretatively using geographic information system (GIS) based characteristic importance ranking results. enables crashes discovers important leading with an explanation. study proposes some areas consideration should be given adding measures nighttime lighting devices fatigue driving alert ensure safe driving. It offers references for policymakers address management urban development issues.

Language: Английский

Citations

4