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

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Март 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.

Язык: Английский

Enhanced artificial hummingbird algorithm with chaotic traversal flight DOI Creative Commons
Juan Du, Jilong Zhang, Shouliang Li

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 29, 2024

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent traditional meta-heuristic algorithms, this study presents enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs ergodicity within foundational framework conventional algorithm. This approach implements motion regions solution space, ensuring a thorough exploration potential preventing algorithmic stagnation at maxima by guaranteeing non-repetitive all search states. also analyzes intrinsic mechanisms eight different mappings affect optimization performance, from perspectives invariant measures efficiency ergodic motion. In comparative tests 21 algorithms on CEC2014, CEC2019, CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior performance. Furthermore, practicability robustness have been confirmed mechanical design problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, piston levers.

Язык: Английский

Процитировано

1
Performance Evaluation of Latest Meta-Heuristic Algorithms in Finding Optimum Value of Mathematical Functions and Problems DOI
Majid Amini‐Valashani, Sattar Mirzakuchaki

Arabian Journal for Science and Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Язык: Английский

Процитировано

1
Green-Box System Identification and Carbon Footprint Analysis for Sustainable Computing DOI

Thalita Nazaré,

Kumars Mahmoodi, Erivelton G. Nepomuceno

и другие.

Опубликована: Июнь 5, 2023

This paper presents the Green-box System Identification approach using multi-objective optimization technique to provide a sustainable computing framework. method applies collected data create simulation models that can represent system behaviour and enhance performance. considers both computational complexity of arithmetic operations environmental impact simulations. The offers uses gCO2 e concerning (CCAO). Karatsuba's algorithm for polynomial multiplication has been incorporated decrease computer duration, which directly affects CO xmlns:xlink="http://www.w3.org/1999/xlink">2 emissions consequently enlarges carbon footprint. To evaluate gCO e, correlation between CCAO computation time basic ascertained. procedure aims minimise while ensuring model's accuracy sustainability. exhibit growing interest in energy sources, proposed methodology was successfully implemented two separate systems: DC motor/generator WEC-Sim model an RM3 dual-body floating-point absorber.

Язык: Английский

Процитировано

2
Analysis of Capacitive Energy Storage Integration on Load Frequency Control Performance of Microgrids with a New Aoa-Optimized Dual-Stage Piλ − (1 + Pdμf) Controller DOI
Bhuvnesh Khokhar,

K. P. Singh Parmar

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Процитировано

0
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

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Март 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.

Язык: Английский

Процитировано

0