RESEARCH ON THE REMAINING INTENSITY OF PIPELINE CORROSION BASED ON IWOA-LSSVM by ZHANG Jia, Ll LinFeng, WANG HaoJie, ZHANG Ting

“…In response to pipeline corrosion surplus intensity, a surplus intensity prediction method based on the Improved Whale Optimization Algorithm (IWOA ) -Least Square Support Vector Machine (LSSVM) combination algorithm model. …”
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Optimization of Flexible Rotor for Ultrasonic Motor Based on Response Surface and Genetic Algorithm by Bo Chen, Jiyue Yang, Haoyu Tang, Yahang Wu, Haoran Zhang

“…This paper presents an optimization method that combines the Kriging response surface model with a multi-objective genetic algorithm (MOGA). …”
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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Convolutional Neural Networks are used to extract spatial features from agricultural data, while Long Short-Term Memory networks processed time series data. To enhance model performance, the slime mould algorithm is employed for parameter optimization. …”
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Optimization of the Driving Mode Transformation of Hybrid Electric Bus based on Genetic Algorithm by Zhao Lijun, Li Shoucheng, Wei Guangpu, Zhang Hongsheng

“…The hybrid electrical city bus has two power sources to drive vehicle,so the control of vehicle driving mode transformation is become the core of the vehicle control strategy.It not only influences the vehicle fuel economy and emissions,but also influences the driving comfort of the vehicle.An HEV simulation model is built for performance analysis by interlinking of advanced software AVL CRUISE and Matlab/Simulink.By adjusting threshold of the driving mode transformation,the mode can be transformed smoothly.For achieving minimum equivalent fuel consumption,the genetic algorithm（GA）optimization method is applied to get the optima of control parameters which affect the vehicle driving mode transformation in different city bus cycles including Europe and China.The vehicle fuel economy is further improved.…”
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Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm by M. Gorji-Bandpy, A. Mozaffari

“…The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). …”
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An Inter-Regional Lateral Transshipment Model to Massive Relief Supplies with Deprivation Costs by Shuanglin Li, Na Zhang, Jin Qin

“…Massive relief supplies inter-regional lateral transshipment (MRSIRLT) can significantly enhance the efficiency of disaster response, meet the needs of affected areas (AAs), and reduce deprivation costs. This paper develops an integrated allocation and intermodality optimization model (AIOM) to address the MRSIRLT challenge. …”
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An image processing technique for optimizing industrial defect detection using dehazing algorithms. by Xuanyi Zhao, Xiaohan Dou, Gengpei Zhang

“…The overall performance surpasses CNN and MADNet models, and verification using the improved YOLOv8 model significantly enhances defect detection confidence while greatly reducing missed detections. …”
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Optimizing Deep Learning Algorithms for Effective Chicken Tracking through Image Processing by Saman Abdanan Mehdizadeh, Allan Lincoln Rodrigues Siriani, Danilo Florentino Pereira

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DQN Simulation Model of Green Cooperative Market Behavior of Multi-Load Users for Optimal Evolution of New-Type Power System by ZHANG Shuo, YUAN Chunhui, LI Yingzi, XIAO Yangming, WEI Ming, HE Yunzheng

“…[Methods] First， the green cooperative market behavior and bidding behavior of multiple loads were analyzed and a multiple-load market bidding decision model was constructed. Second， the decision-making process of multiple loads and the entire transaction simulation process of the power market were constructed， and the deep reinforcement learning DQN algorithm was applied to analyze the behavior of multiple loads participating in market transactions and obtain their optimal bidding strategies. …”
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Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions by Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang, Siwei Zhang

“…To effectively mitigate these limitations, this work proposes a dual-stage feature extraction method based on Variational Mode Decomposition (VMD) and Principal Component Analysis (PCA), enhancing multi-scale modeling and noise reduction capabilities. Additionally, the Whale Optimization Algorithm is adopted to efficiently optimize the hyperparameters of iTransformer for the framework, improving parameter adaptability and convergence efficiency. …”
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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…Abstract The low-cost sensor has changed the air quality monitoring paradigm with the capacity for efficient network expansion and community engagement. …”
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Optimization of particle filter tracking algorithm based on weakly supervised attribute learning by Hui Zhang, Dawang Shen

“…Abstract This study proposes an optimization method for particle filter tracking algorithm to solve the issues of low recognition efficiency and poor tracking accuracy faced by existing target tracking algorithms in complex environments. …”
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Optimal Scheduling Method for Power Generation of Cascade Reservoirs Based on RLDE Algorithm by CHEN Jia-wen, ZHU Xin, TANG Zheng-yang, SHEN Ke-yan, CHEN Xiao-lin, QIN Hui

“…To validate the feasibility and effectiveness of the RLDE algorithm, it was applied to optimize the power generation scheduling model for four major cascade reservoirs (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) on the lower Jinsha River. …”
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Optimization and Validation of Wafer Surface Defect Detection Algorithm Based on RT-DETR by Ao Xu, Yanwei Li, Hongbo Xie, Rui Yang, Jianjie Li, Jiaying Wang

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Optimizing piezoelectric actuator placement for enhanced vibration control using genetic algorithms by Shuqing Wang, Huichao Jin, Yu Wang, Junyi Huo, Xudong Liu

“…This study proposes an optimization framework for piezoelectric sensor and actuator configurations in cantilever beams using a genetic algorithm. …”
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Optimization Strategies for Electric Vehicles: A Review of Evolutionary, Swarm, and Hybrid Algorithms by Kumar A. Vijay, Manohar V. Joshi

“…Future research directions include exploring hybrid algorithms and adaptive optimization approaches to further improve EV performance.…”
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Optimization of Communication Quality for Energy-Limited Inspection AAV: A Hybrid Algorithm by Wei Wang, Jiangling Cao, Dingcheng Yang, Hao He, Zhihai Xu

“…Numerical results show that the proposed algorithm is effective in optimizing the communication quality of the inspection AAV with limited energy, and its performance is improved by about 15%-50% compared with other benchmarks.…”
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An improved Red-billed blue magpie feature selection algorithm for medical data processing. by Chenyi Zhu, Zhiyi Wang, Yinan Peng, Wenjun Xiao

“…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”
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Optimizing Bi-LSTM networks for improved lung cancer detection accuracy. by Su Diao, Yajie Wan, Danyi Huang, Shijia Huang, Touseef Sadiq, Mohammad Shahbaz Khan, Lal Hussain, Badr S Alkahtani, Tehseen Mazhar

“…We employed traditional hand-crafted features, such as Gray Level Co-occurrence Matrix (GLCM) features, in conjunction with traditional machine learning algorithms. To explore the potential of deep learning, we also optimized and implemented a Bidirectional Long Short-Term Memory (Bi-LSTM) network for lung cancer detection. …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

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