Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm by Zhuang Liu, Ning Yang, Jiaxing Fu, Huanqing Cai, Xuebei Wei

“…The results show that under flight environments with varying complexity of obstacle and threat region distribution, compared with the Particle Swarm Optimization algorithm (PSO), Butterfly Optimization Algorithm (BOA), and Reptile Search Algorithm (RSA), NWFRSA can effectively reduce the path cost (by 4.53%–34.47%), contributing to improved.…”
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Drilling Parameter Control Based on Online Identification of Drillability and Multi-Objective Optimization by Jianbo Dai, Xilu Yin, Yan Zhang, Lei Si, Dong Wei, Zhongbin Wang, Longmei Zhao

“…A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. …”
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Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform by Yinglei Li, Qingping Hu, Shiyan Sun, Yuxiang Zhou, Wenjian Ying

“…Subsequently, the Taylor series expansion method is adopted to linearize the approximated coordinate transformation process and simplify the error propagation model. Finally, in order to further improve the positioning accuracy, a target position correction strategy with the improved dung beetle optimization algorithm is introduced. …”
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Inventory Optimization of Fresh Agricultural Products Supply Chain Based on Agricultural Superdocking by Lixin Shen, Fucheng Li, Congcong Li, Yumin Wang, Xueqi Qian, Tao Feng, Cong Wang

“…An improved genetic algorithm is developed to solve the nonlinear optimization problem. …”
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Optimal strategy selection method for moving target defense based on signaling game by Lyu JIANG, Hengwei ZHANG, Jindong WANG

“…To solve the problem of the optimal strategy selection for moving target defense,the defense strategy was defined formally,the defense principle from the perspective of attack surface shifting and exploration surface enlarging was taken into account.Then,network attack-defense behaviors were analyzed from the sight of dynamic confrontation and bounded information.According to the analysis of attack-defense game types and confrontation process,the moving target defense model based on signaling game was constructed.Meanwhile,the method to quantify strategies was improved and the solution of perfect Bayesian equilibrium was proposed.Furthermore,the optimal defense strategy selection algorithm was designed by the equilibrium analysis.Finally,the simulation demonstrates the effectiveness and feasibility of the proposed optimal strategy and selection method.…”
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An Improved PSO-Based DC Discharge Heating Strategy for Lithium-Ion Batteries at Low Temperatures by Shaojian Han, Chengwei Li, Jifeng Ding, Xinhua Gao, Xiaojie Li, Zhiwen Zhang

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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…To enhance the capacity of the classification system to predict multiple classes, preprocessing procedures such as data reduction, cleaning, and transformation were used on the raw vitamin D dataset. The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. …”
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Application of inertial navigation high precision positioning system based on SVM optimization by Ruiqun Han

“…Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. …”
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Weighted Filter Bank and Regularization Common Spatial Pattern-Based Decoding Algorithm for Brain-Computer Interfaces by Jincai Ye, Jiajie Zhu, Shoulin Huang

“…The algorithm divides the signal into multiple frequency bands, adaptively assigns subject weights based on the mutual information maximization criterion, and optimizes the covariance matrix with a regularization strategy, significantly improving the robustness of feature extraction. …”
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Reconstruction of porous media pore structure and simulation effect analysis of multi-index based on SNESIM algorithm by Qing Xie, Jiaqi Gao, Xiaochuang Ye, Jia Li, Yifei Song, Siwen Hu

“…In particular, when dealing with complex pore structures, the accuracy and performance of the SNESIM algorithm need further improvement. Future research will focus on optimizing the algorithm to handle more diverse pore structures and exploring 3D reconstruction methods to more comprehensively describe and analyze the pore characteristics in actual porous media.…”
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Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization by Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado

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Efficient Task Scheduling in Cloud Computing: A Multiobjective Strategy Using Horse Herd–Squirrel Search Algorithm by V. Parthasaradi, A. Karunamurthy, C. H. Hussaian Basha, S. Senthilkumar

“…The novel algorithm used here merges two familiar algorithms as squirrel search algorithm (SSA) and the horse herd optimization algorithm (HOA) leading to a new hybrid metaheuristic algorithm called the horse herd–squirrel search algorithm (HO–SSA). …”
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Damage Identification in Large-Scale Structures Using Time Series Analysis and Improved Sparse Regularization by Huihui Chen, Xiaojing Yuan

“…Aiming at the existing obstacles, this study enables to propose a novel method based on time series analysis model and improved sparse regularization technique for damage identification of the large-scale structure. …”
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Cancer Diagnosis Optimization With a Combination of Flexible THz Antennas and Machine Learning by M. Senthil Pandian, S. Deepa Nivethika, J. Idhikash, Vamsee N. Yashwanth, Aishwarya Shaji, Prabhakaran Paulraj

“…The optimization of the decision was done using the combination of K‐means and logistic regression algorithm to determine 95.06% efficiency.…”
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An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing by Min Cui, Yipeng Wang

“…Furthermore, an improved Whale Optimization Algorithm (WOA) based on Lévy flight is proposed. …”
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An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things by Mai Alduailij

“…To further optimize model performance, the pelican optimization algorithm (POA) is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. …”
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Logistics Distribution Path Optimization Considering Carbon Emissions and Multifuel-Type Vehicles by Chuanxiang Ren, Li Lu, Juan Teng, Changchang Yin, Juntao Li, Haowei Ji, Xiaoqi Wang, Fangfang Fu

“…An improved genetic algorithm (IGA) is designed to solve the VRP-CEMF. …”
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Individualized post‐operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near‐infrared spectroscopy by Zhe Chen, Xue Zhao, Haotian Liu, Yuyang Wang, Zhikai Zhang, Yuxuan Zhang, Yuhe Liu

“…Both classification and individualized regression models were constructed to predict post‐CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms. …”
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Capacity planning for wind, solar, thermal and energy storage in power generation systems considering coupled electricity‐carbon markets by Jiajia Huan, Yuling He, Kai Sun, Hongchang Lu, Haipeng Wang, Xuewei Wu

“…The model employs a bi‐level optimization method based on the Improved Coati Optimization Algorithm (ICOA) to optimize the system's capacity planning. …”
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Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m by Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, Yong Bae Seo, Dong-Hyun Ahn, Gun-Do Kim

“…This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. …”
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