Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem DOI
Tingyu Ye, Fang Li, Hui Wang

и другие.

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112601 - 112601

Опубликована: Дек. 1, 2024

Язык: Английский

A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem DOI
Hongtao Tang, Xiao Yu,

Wei Zhang

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121723 - 121723

Опубликована: Сен. 22, 2023

Язык: Английский

Процитировано

58
Machine learning-driven 3D printing: A review DOI
Xijun Zhang,

Dianming Chu,

Xinyue Zhao

и другие.

Applied Materials Today, Год журнала: 2024, Номер 39, С. 102306 - 102306

Опубликована: Июнь 29, 2024

Язык: Английский

Процитировано

18
An indoor blind area-oriented autonomous robotic path planning approach using deep reinforcement learning DOI
Yuting Zhou, Junchao Yang, Zhiwei Guo

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 254, С. 124277 - 124277

Опубликована: Май 20, 2024

Язык: Английский

Процитировано

16
Efficient Q-learning Hyperparameter Tuning Using FOX Optimization Algorithm DOI Creative Commons

Mahmood A. Jumaah,

Yossra H. Ali, Tarik A. Rashid

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104341 - 104341

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms DOI
Md Hafizur Rahman, Muhammad Majid Gulzar, Tansu Sila Haque

и другие.

Engineering Science and Technology an International Journal, Год журнала: 2025, Номер 64, С. 101950 - 101950

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

1
Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms DOI Creative Commons
Gamil Ahmed, Tarek Sheltami, Mustafa Ghaleb

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(6), С. 2418 - 2418

Опубликована: Март 13, 2024

The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge safety drones. One key challenges IoD formation path planning, which involves determining optimal paths all UAVs while avoiding obstacles other constraints. Limited battery life another that limits operation time UAVs. To address these issues, drones require efficient collision avoidance energy-efficient strategies effective planning. This study focuses on using meta-heuristic algorithms, recognized robust global optimization capabilities, solve path-planning problem. We model problem an aims minimize energy consumption considering threats posed obstacles. Through extensive simulations, this research compares effectiveness particle swarm (PSO), improved PSO (IPSO), comprehensively (CIPSO), artificial bee colony (ABC), genetic algorithm (GA) optimizing IoD’s planning obstacle-dense environments. Different performance metrics have been considered, such optimality, consumption, straight line rate (SLR), relative percentage deviation (RPD). Moreover, nondeterministic test applied, one-way ANOVA obtained validate results different algorithms. Results indicate IPSO’s superior terms stability, convergence speed, length efficiency, albeit with longer run compared ABC.

Язык: Английский

Процитировано

7
Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis DOI
Jinge Shi, Yi Chen, Zhennao Cai

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 96, С. 106492 - 106492

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

6
A Q-learning based artificial bee colony algorithm for solving surgery scheduling problems with setup time DOI
Ruixue Zhang, Hui Yu, Kaizhou Gao

и другие.

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 90, С. 101686 - 101686

Опубликована: Авг. 9, 2024

Язык: Английский

Процитировано

5
Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey DOI
Yang Yang, Yuchao Gao, Zhe Ding

и другие.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(6)

Опубликована: Авг. 18, 2024

Abstract This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects QLMA, including parameter adaptation, operator selection, and balancing global exploration local exploitation. QLMA has become a leading solution industries like energy, power systems, engineering, addressing range mathematical challenges. Looking forward, we suggest further integration, transfer learning strategies, techniques to reduce state space. article is categorized under: Technologies > Computational Intelligence Artificial

Язык: Английский

Процитировано

5
Artificial bee colony algorithm based on multi-neighbor guidance DOI
Xinyu Zhou,

Guisen Tan,

Hui Wang

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 259, С. 125283 - 125283

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

4