SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments DOI Creative Commons
Meng Zheng,

Qing He,

Q. H. He

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 15, 2024

Abstract Unmanned Aerial Vehicle (UAV) route planning is an intricate issue that requires the comprehensive consideration of multiple factors and combination suitable strategies to achieve efficient safe flight paths. Its purpose plan a navigational path for drone specific task or avoid obstacles. In practical applications, UAVs are usually required accomplish tasks in variety complex environments, resulting feasible paths will be reduced becomes difficult. Accordingly, we design simple yet useful planner, called Self-adaptive Golden Equilibrium Optimization algorithm (SGEO). The proposed combines new self-adaptive approach, acceleration control factor Golden-SA strategy order balance exploitation exploration capabilities. novel used expand global search range enhance its ability exploration; introduced parameters a1 a2 improve convergence algorithm; helps candidate particles better between different dimensions based on golden ratio sine function, this enables explore space more comprehensively. limitations UAV translated into objective four algorithms compare with SGEO two circumstances. simulation findings reveal excels at three-dimensional complicated environments.

Language: Английский

A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems DOI
Betül Sultan Yıldız, Sumit Kumar, Natee Panagant

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 271, P. 110554 - 110554

Published: April 10, 2023

Language: Английский

Citations

85
An improved algorithm optimization algorithm based on RungeKutta and golden sine strategy DOI
Mingying Li, Zhilei Liu,

Hongxiang Song

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123262 - 123262

Published: Jan. 25, 2024

Language: Английский

Citations

17
Reptile Search Algorithm: Theory, Variants, Applications, and Performance Evaluation DOI
Buddhadev Sasmal, Abdelazim G. Hussien, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(1), P. 521 - 549

Published: Aug. 26, 2023

Language: Английский

Citations

27
Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation DOI
Buddhadev Sasmal, Arunita Das, Krishna Gopal Dhal

et al.

Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100727 - 100727

Published: Jan. 18, 2025

Language: Английский

Citations

1
Improving flood-prone areas mapping using geospatial artificial intelligence (GeoAI): A non-parametric algorithm enhanced by math-based metaheuristic algorithms DOI
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Farman Ali

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124238 - 124238

Published: Jan. 29, 2025

Language: Английский

Citations

1
Chaotic Puma Optimizer Algorithm for controlling wheeled mobile robots DOI Creative Commons

Mohamed Kmich,

Nawal El Ghouate,

Ahmed Bencharqui

et al.

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 63, P. 101982 - 101982

Published: Feb. 2, 2025

Language: Английский

Citations

1
Optimal allocation of distribution generation sources with sustainable energy management in radial distribution networks using metaheuristic algorithm DOI
Amit Chakraborty, Saheli Ray

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 116, P. 109142 - 109142

Published: Feb. 29, 2024

Language: Английский

Citations

6
A Comprehensive Survey on African Vulture Optimization Algorithm DOI
Buddhadev Sasmal, Arunita Das, Krishna Gopal Dhal

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(3), P. 1659 - 1700

Published: Nov. 30, 2023

Language: Английский

Citations

13
Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey DOI
Yang Yang, Yuchao Gao, Zhe Ding

et al.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Aug. 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

Language: Английский

Citations

4
Performance evaluation of submerged FSW for aluminum 6061 employing AOA-ANN based on experimental data DOI

Ekta Shrivastava,

Pankaj Shrivastava, Pushpendra Singh

et al.

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 9, 2025

Submerged friction stir welding (SFSW) under water is a relatively new solid state joining process, which combines heating and mechanical work for deformation to achieve high quality, defect-free joints. In the present research aluminum-6061 alloy has been welded by using SFSW. Tool rotational speed, feed temperature taken as important control variables estimate joint performance in terms of hardness tensile strength. It was observed that average grain size obtained zone around 3.5 μm, maximum 87 HV strength 175 MPa. Predictive models artificial neural networks (ANNs) were developed both strength, followed process optimization utilizing four distinct evolutionary techniques: arithmetic algorithm (AOA), Jaya Rao-3 algorithm. Among these, AOA demonstrated superior within manufacturing environment. As compared experimental values AOA, show improvement 6.33% 0.35% respectively.

Language: Английский

Citations

0