Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…The K-Nearest Neighbors (KNN) algorithm is employed as the base model, with hyperparameter optimization performed using GridSearchCV, RandomizedSearchCV, Optuna, and Particle Swarm Optimization (PSO). …”
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Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. …”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. …”
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Shape Optimization of Multi-chamber Acoustical Plenums Using the BEM, Neural Networks, and the GA Method by Ying-Chun CHANG, Ho-Chih CHENG, Min-Chie CHIU, Yuan-Hung CHIEN

“…The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.…”
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A VNS-NPGA approach to multi-objective optimization of hub-and-spoke logistics network by Changxi Ma, Chuwei Shi, Yun Yang, Yongpeng Zhao, Zhuye Xu, Bo Du

“…To realize low-cost freight transport in the logistics network and improve the network operation efficiency, a multi-objective optimization model and the corresponding algorithm for a hub-and-spoke logistics network are proposed based on the multi-level location of hub points and channels layout. …”
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Facility location problem for senior centers in an upcoming super-aging society by Eun Hak Lee, Jonghwa Jeong

“…This study aims to address the facility location problem for senior centers in upcoming super-aging societies. An optimization model is developed using a genetic algorithm to determine the optimal locations of senior centers. …”
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Energy optimization through morphing blade design under structural constraints: a case study on the NREL 1.5 MW wind turbine by Najafian Arezoo, Jahangirian Alireza

“…The morphing process is modeled using an m-degree shape function and optimized through a Genetic Algorithm (GA) to maximize power generation while minimizing structural displacement and thrust forces. …”
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Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention by Wei Liu, Yu Cao

“…This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. …”
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Application of a grey wolf optimization-enhanced convolutional neural network and bidirectional gated recurrent unit model for credit scoring prediction. by Yetong Fang

“…CNN performs well in feature extraction and can effectively capture patterns in customer historical behaviors, while BiGRU is good at handling time dependencies, which further improves the prediction accuracy of the model. The GWO algorithm is introduced to further improve the overall performance of the model by optimizing key parameters. …”
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Tailoring orthotropic mechanical and thermal properties in nonlinear contact, vibration and thermal problems via kriging-based optimization by Yicong Zhou, Jinyan Duan, Guoxin Mao, Jun Hong

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Mixed Time/Event-Triggered Model Predictive Tracking Control for Networked Mobile Robots by Huixin Liu, Yonghua Lai, Hongsong Lian, Guobin Wang, Dongsheng Zheng

“…The MPC algorithm is developed based on the auxiliary optimization problem (OP), which is constrained by linear matrix inequalities. …”
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Sensorless Control of Ultra-High-Speed PMSM via Improved PR and Adaptive Position Observer by Xiyue Bai, Weiguang Huang, Chuang Gao, Yingna Wu

“…To improve the precision of the position and speed estimation in ultra-high-speed (UHS) permanent magnet synchronous motors (PMSM) without position sensors, multiple refinements to the traditional extended electromotive force (EEMF) estimation algorithm are proposed in this paper. …”
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A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties by Haowen Xu, Minglei Bao, Xun Yao, Xiaocong Sun, Yi Ding, Zhenglin Yang

“…The proposed model is structured as a bi-level optimization problem to reflect the interaction between WSS bidding and day-ahead market clearing. …”
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FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer by Dongyan Wang, Limin Chen, Xiaotong Lu, Yidi Wang, Yue Shen, Jingjing Xu

“…This paper proposes a federated learning approach based on the dung beetle optimizer, named FedDBO. In this method, the model parameters uploaded from clients to the server are transformed into model scores. …”
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Two-Stage Dispatch of CCHP Microgrid Based on NNC and DMC by Suhao CHEN, Yue WU, Wei ZENG, Xiaohui YANG, Xiaopeng WANG, Yunfei WU

“…In the online optimization stage, a finite-time domain optimization model based on dynamic matrix control algorithm is established to track and optimize the offline optimization results with feedback correction to reduce the influence of uncertainty factors. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Among the machine learning methods used for modelling, the prediction results of the tree model are improved to a certain degree after the error-correcting output code optimization. …”
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Balanced operation strategies of district heating systems based on dynamic hydraulic-thermal modeling by Xiaojie Lin, Ning Zhang, Zheng Luo, Encheng Feng, Wei Zhong

“…For the primary side network in the various operation conditions, the dynamic hydraulic-thermal model is established. The heating supply imbalance of the primary side return temperatures among different heating substations is defined and optimized based on the dynamic hydraulic-thermal model and the particle swarm optimization algorithm. …”
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Optimized fiducial marker placement using B‐spline surface modeling and graph theory for Cyberknife stereotactic body radiotherapy for superficial tumors by Jing Huang, Xianlong Xiong, Cheng Chen, Yuhan Li, Ruijie Wang, Zhitao Dai

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Game algorithm based on link quality: Wireless sensor network routing game algorithm based on link quality by Zhanjun Hao, Jiaojiao Hou, Jianwu Dang, Xiaochao Dang, Nanjiang Qu

“…In the simulation experiment, the influence of the change of link quality parameters on the performance of the algorithm is analyzed, and the proposed algorithm is compared with non-linear weight particle swarm optimization (NWPSO) algorithm and Low Energy Adaptive Clustering Hierarchy-Improvement (LEACH-IMPT) algorithm in three aspects: the number of surviving nodes, network lifetime, and network energy consumption. …”
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A two-stage robust optimization model for emergency service facilities location-allocation problem under demand uncertainty and sustainable development by Hongyan Li, Dongmei Yu, Yiming Zhang, Yifei Yuan

“…By adequately accounting for potential uncertainties and carrying out comprehensive pre-planning, the robustness of location-allocation decisions can be significantly improved. This paper delves into the ESF network design problem under demand uncertainty and formulates this problem as a two-stage robust optimization model. …”
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