A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems DOI Creative Commons
Shi Wang, Sheng Li, Hang Yu

et al.

Electronic Research Archive, Journal Year: 2024, Volume and Issue: 32(7), P. 4659 - 4683

Published: Jan. 1, 2024

<p>The focus on clean energy has significantly increased in recent years, emphasizing eco-friendly sources like solar, wind, hydropower, geothermal, and biomass energy. Among these, wind energy, utilizing the kinetic from is distinguished by its economic competitiveness environmental benefits, offering scalability minimal operational emissions. It requires strategic turbine placement within farms to maximize conversion efficiency, a complex task involving analysis of patterns, spacing, technology. This traditionally been tackled meta-heuristic algorithms, which face challenges balancing local exploitation with global exploration integrating problem-specific knowledge into search mechanism. To address these challenges, an innovative power generation accumulation-based adaptive chaotic differential evolution algorithm (ACDE) proposed, enhancing conventional approach adjustment strategy based tournament selection. aimed prioritize energy-efficient positions improve population diversity, thereby overcoming limitations existing algorithms. Comprehensive experiments varying rose configurations demonstrated ACDE's superior performance showcasing potential optimizing for enhanced production. The farm layout optimization competition hosted Genetic Evolutionary Computation Conference provided comprehensive set layouts. dataset was utilized further validate results unequivocally demonstrate superiority ACDE when tackling problems.</p>

Language: Английский

Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization DOI Creative Commons
Huanyu Liu, Jiahao Luo, Lihan Zhang

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(3), P. 233 - 233

Published: Jan. 22, 2025

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in collaborative harvesting corn by harvesters grain transport vehicles hilly areas. A path-planning scheduling method is proposed, combining Dijkstra’s algorithm with Improved Harris Hawk Optimization (IHHO) algorithm. field model based on Digital Elevation Model (DEM) data created for full coverage path planning, reducing length. transfer road network established, used to calculate distances between fields. multi-objective then developed minimize costs, time. The IHHO enhances search performance introducing quantum initialization improve initial population, integrating slime mold better exploration, applying an average differential mutation strategy nonlinear energy factor updates strengthen both global local search. Non-dominated sorting crowding distance techniques are incorporated enhance solution diversity quality. results show that compared traditional HHO algorithms, reduces 4.2% 14.5%, time 4.5% 8.1%, consumption 3.5% 3.2%, respectively. approach effectively saves energy, improves operational providing valuable insights planning multi-field transportation

Language: Английский

Citations

2
Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimization DOI
Rui Zhong,

Qinqin Fan,

Chao Zhang

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(7), P. 10141 - 10168

Published: May 4, 2024

Language: Английский

Citations

13
DEA$$^2$$H$$^2$$: differential evolution architecture based adaptive hyper-heuristic algorithm for continuous optimization DOI
Rui Zhong, Jun Yu

Cluster Computing, Journal Year: 2024, Volume and Issue: unknown

Published: June 8, 2024

Language: Английский

Citations

13
LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm DOI
Rui Zhong, Abdelazim G. Hussien, Jun Yu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 64, P. 103042 - 103042

Published: Jan. 5, 2025

Language: Английский

Citations

0
A Monte Carlo hyper-heuristic algorithm with low-level heuristics reward prediction for missile path planning DOI
Shuangfei Xu, Zhanjun Huang, Wenhao Bi

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)

Published: Jan. 7, 2025

Language: Английский

Citations

0
Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm DOI Creative Commons
Yang Cao, Jun Yu, Rui Zhong

et al.

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

Published: Jan. 20, 2025

This research utilizes time series models to forecast electricity generation from renewable energy sources and consumption. The configuration of optimal parameters for these typically requires optimization algorithms, but conventional algorithms may struggle with fixed search patterns limited robustness. To address this, we propose an auto-evolution hyper-heuristic algorithm named AE-GAPB. AE-GAPB integrates a genetic (GA) at the high-level component employs particle swarm (PSO) bat (BA) low-level component. GA continuously finds best hyperparameters PSO BA based on prediction accuracy, which significantly accelerates improves accuracy. Additionally, crossover mutation rates evolve over iteration fitness value space, further enhancing its adaptability. We validated six forecasting compared it five well-known as well GAPB without As result, achieved excellent results consumption datasets Hokkaido, Kyushu, Tohoku regions Japan.

Language: Английский

Citations

0
Design and Optimization of an Internet of Things-Based Cloud Platform for Autonomous Agricultural Machinery Using Narrowband Internet of Things and 5G Dual-Channel Communication DOI Open Access

Baidong Zhao,

Dong Zheng, Chenghan Yang

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(8), P. 1672 - 1672

Published: April 20, 2025

This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency operational performance autonomous agricultural machinery. The integrates dual capabilities Narrowband (NB-IoT) 5G, where NB-IoT is utilized low-power, reliable data transmission from environmental sensors, such as soil information weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling path tracking to effectively address diverse requirements modern complex scenarios. improves resource utilization through real-time scheduling, dynamic optimization, seamless coordination between devices. To accommodate demands environments, system incorporates feedback mechanism leveraging sensor adjustment, adaptability stability. Furthermore, multi-machine collaborative strategy combining Dijkstra’s algorithm improved Harris hawk (IHHO) algorithm, along with multi-objective optimized method, introduced further improve improving accuracy smoothness reducing external interferences, including fluctuations inaccuracies. Experimental results demonstrate that IoT-based excels in reliability, accuracy, validating its feasibility smart agriculture providing efficient scalable solution large-scale operations.

Language: Английский

Citations

0
Leveraging large language model to generate a novel metaheuristic algorithm with CRISPE framework DOI
Rui Zhong, Yuefeng Xu, Chengqi Zhang

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 13835 - 13869

Published: July 6, 2024

Language: Английский

Citations

3
Mathematical modeling and problem solving: from fundamentals to applications DOI Creative Commons
Masahito Ohue,

Kotoyu Sasayama,

Masami Takata

et al.

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(10), P. 14116 - 14119

Published: March 15, 2024

Abstract The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are focus this special issue. This issue compiles extensively revised improved versions top papers from workshop on Mathematical Modeling Problem Solving at PDPTA'23, 29th International Conference Parallel Distributed Processing Techniques Applications. Covering fundamental research matrix operations heuristic searches to real-world applications computer vision drug discovery, underscores crucial role supercomputing parallel distributed computing infrastructure research. Featuring nine key studies, pushes forward computational technologies refines techniques for analyzing images time-series data, introduces new methods pharmaceutical materials science, making significant contributions these areas.

Language: Английский

Citations

1
A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems DOI Creative Commons
Shi Wang, Sheng Li, Hang Yu

et al.

Electronic Research Archive, Journal Year: 2024, Volume and Issue: 32(7), P. 4659 - 4683

Published: Jan. 1, 2024

<p>The focus on clean energy has significantly increased in recent years, emphasizing eco-friendly sources like solar, wind, hydropower, geothermal, and biomass energy. Among these, wind energy, utilizing the kinetic from is distinguished by its economic competitiveness environmental benefits, offering scalability minimal operational emissions. It requires strategic turbine placement within farms to maximize conversion efficiency, a complex task involving analysis of patterns, spacing, technology. This traditionally been tackled meta-heuristic algorithms, which face challenges balancing local exploitation with global exploration integrating problem-specific knowledge into search mechanism. To address these challenges, an innovative power generation accumulation-based adaptive chaotic differential evolution algorithm (ACDE) proposed, enhancing conventional approach adjustment strategy based tournament selection. aimed prioritize energy-efficient positions improve population diversity, thereby overcoming limitations existing algorithms. Comprehensive experiments varying rose configurations demonstrated ACDE's superior performance showcasing potential optimizing for enhanced production. The farm layout optimization competition hosted Genetic Evolutionary Computation Conference provided comprehensive set layouts. dataset was utilized further validate results unequivocally demonstrate superiority ACDE when tackling problems.</p>

Language: Английский

Citations

0