<b>Low-Carbon Optimization Scheduling of Multi-Energy Coupled Virtual Power Plants Based on an Improved Gray Wolf Optimization Algorithm</b> by f y (22066025)

“…<p dir="ltr"><b>Low-Carbon Optimization Scheduling of Multi-Energy Coupled </b><a href="" target="_blank"><b>Virtual Power Plant</b></a><b>s Based on an Improved Gray Wolf Optimization Algorithm</b></p>…”

An improved Grey Wolf Optimization based heuristic initialization algorithm for feature selection in P2P lending default prediction by Muhammad Sam'an (20649621)

Grey Wolf hierarchy. by Genliang Li (5816264)

“…To address this, we introduce an Adaptive Mechanism-based Grey Wolf Optimizer (AMGWO) for FS in high-dimensional classification. …”

Noise reduction performance of 2D seismic profile based on GWO-VMD. by Zhenjing Yao (22189970)

Final optimization results. by Dawei Wang (471687)

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Optimize process data and final results by Dawei Wang (471687)

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Optimize comparison of whether cto set penalty values. by Dawei Wang (471687)

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Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling by He Wang (250350)

“…This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. …”

Optimized unbalanced train operation chart. by Zhipeng Huang (1759759)

“…<div><p>High-speed railway timetables are typically based on origin-destination (OD) passenger demand, establishing departure times and intervals for trains. …”

Image 1_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif by Tingkai Hou (21014762)

“…</p>Methods<p>This paper proposes a displacement back-analysis (DBA) approach that utilizes support vector regression (SVR) optimized by differential evolution grey wolf algorithm (DE-GWO) to invert the RMMPs, which improves global optimization capability and inversion accuracy. …”

Image 2_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif by Tingkai Hou (21014762)

“…</p>Methods<p>This paper proposes a displacement back-analysis (DBA) approach that utilizes support vector regression (SVR) optimized by differential evolution grey wolf algorithm (DE-GWO) to invert the RMMPs, which improves global optimization capability and inversion accuracy. …”

Noise reduction performance comparison on synthetic Ricker profile. by Zhenjing Yao (22189970)

Noise reduction performance comparison on synthetic Bump data. by Zhenjing Yao (22189970)

Noise reduction performance comparison on synthetic Ricker data. by Zhenjing Yao (22189970)

Sample output for grad-cam-based interpretability analysis. by Hasim Khan (21647570)

Noise reduction performance on real field seismic data of EW direction at Setouchi city. by Zhenjing Yao (22189970)

Noise reduction performance on real field seismic data of UD direction at Setouchi city. by Zhenjing Yao (22189970)

Noise reduction performance on real field seismic data of NS direction at Setouchi city. by Zhenjing Yao (22189970)

Ricker seismic profile. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

Noise reduction on testing sets from STEAD. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”