Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method by Teng Jing, Yu Yang, Weimin Ru

“…Specifically, it achieved a rise time of 0.0049 s, a settling time of 0.0079 s, and a steady-state error of 0.0013 mm. The improved PSO algorithm ensures system stability while delivering faster dynamic response and superior control accuracy.…”
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Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model by Guowei Li, Gang Tang, Jingyu Zhang, Qun Sun, Xiangjun Liu

“…The paper then delves into the realization method of ship heave motion based on PSO-CNN-LSTM, where the convolutional neural network (CNN) is used to extract the features of the input signal, thereby enhancing the multi-source feature fusion ability of the LSTM neural network model. The PSO algorithm is then employed to optimize the network structure and hyperparameters of the convolutional neural network. …”
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Failure thresholds and weak part identification in cascade reservoir system: A risk-based optimization framework by Haibin Wang, Jiahong Liu, Chao Mei, Jia Wang, Tianxu Song

“…Secondly, a dynamic Particle Swarm Optimization-Genetic Algorithm (PSO-GA) model optimizes outflow strategies during flood events. …”
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Adaptive optimization decision system for plate-fin heat Exchangers: An integrated approach to enhancing efficiency and performance by Na Sun, Shuai Zhang, Nan Li, Zijian Li, Meng He, Zhengchun Shen, Ke Wang, Xiaoyong Guo, Wen-Quan Tao

“…The GSA module uses the Sobol method to evaluate the impact of design variables on performance. The optimization module employs the Newton-Raphson-based optimizer (NRBO) and the multi-strategy improved grey wolf optimization algorithm (MIGWO). …”
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Intelligent Flight Procedure Design: A Reinforcement Learning Approach with Pareto-Based Multi-Objective Optimization by Yunyang Huang, Yanxin Zhang, Yandong Zhu, Zhuo Zhang, Longtao Zhu, Hongyu Yang, Yulong Ji

“…To further enhance performance by tackling the low sampling efficiency in the replay buffer, we introduce a multi-objective sampling strategy based on the Pareto frontier, integrated with the soft actor–critic (SAC) algorithm. Experimental results demonstrate that the proposed method generates executable flight procedures in the BlueSky open-source flight simulator, successfully balancing these three conflicting objectives, while achieving a 28.6% increase in convergence speed and a 4% improvement in comprehensive performance across safety, route simplification, and environmental impact compared to the baseline algorithm. …”
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Optimization of Smooth Trajectories for Two-Wheel Differential Robots Under Kinematic Constraints Using Clothoid Curves by Wei Zeng, Tifan Xiong, Chao Wang

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Route Optimization of Hazardous Material Railway Transportation Based on Conditional Value-at-Risk Considering Risk Equity by Liping Liu, Shilei Sun, Shuxia Li

“…This research offers practical decision support for transport companies and regulators, helping optimize routes while ensuring cost efficiency and regulatory compliance. …”
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Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics by Hennings, Felix, Hoppmann-Baum, Kai, Zittel, Janina

“…In addition, we introduce a new algorithmic approach that is based on splitting the complexity of the problem by first finding assignments for discrete variables and then determining the continuous variables as locally optimal solution of the corresponding non-linear program. …”
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Thermo-hydrodynamic and exergy optimization of a photovoltaic thermal (PV/T) air collector using NSGA-II by Seyed Morteza Javadpour, Rasoul Soufi Noughabi, Ebrahim Kadivar

“…The single-objective optimization results indicate a 28 % enhancement in thermal efficiency and an 18 % improvement in exergy efficiency, while hydrodynamic performance sees a significant 98 % reduction. …”
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Cross-Domain Edge Computing Offloading Strategy for Delay-Optimized in Low Earth Orbit Satellite Network by YANG Guisong, HUANG Yechao, HE Xingyu

“…To address these challenges, an edge computing offloading process based on control domain partitioning is designed, and a latency-optimized cross-domain computing offloading strategy using the Actor-Critic algorithm is proposed. …”
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Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming by Yang Yang, Xin Feng, Jing Zhang, Hongwei Yang, Tingting Zheng

“…The MOP aims to minimize the maximum sidelobe level (SLL) and propulsion energy consumption while maximizing the transmission rate. We propose an enhanced multi-objective evolutionary algorithm, BSINSGA-II, which integrates beetle swarm optimization (BSO) into the non-dominated sorting genetic algorithm II (NSGA-II). …”
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Numerical Optimization of Laser Powder Bed Fusion Process Parameters for High-Precision Manufacturing of Pure Molybdenum by İnayet Burcu Toprak, Nafel Dogdu, Metin Uymaz Salamci

“…The findings highlight that optimized parameter selection significantly improves the structural integrity of Mo-based components while minimizing manufacturing defects, thus offering valuable guidance for advancing industrial-scale additive manufacturing of refractory metals.…”
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An Optimization Coverage Strategy for Wireless Sensor Network Nodes Based on Path Loss and False Alarm Probability by Jianing Guo, Yunshan Sun, Ting Liu, Yanqin Li, Teng Fei

“…Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. …”
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Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation by Mohammad Rasoolinejad, Irene Say, Peter B. Wu, Xinran Liu, Yan Zhou, Yan Zhou, Nathan Zhang, Emily R. Rosario, Daniel C. Lu, Daniel C. Lu, Daniel C. Lu

“…The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.MethodsWe conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes. …”
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Two-layer Optimization Scheduling for Off-grid Microgrids Based on Multi-agent Deep Policy Gradient by Huicong FAN, Zhiguo DUAN, Zhiyong CHEN, Shijia ZHU, Hang LIU, Wenxiao LI, Yang YANG

“…The lower-level model optimizes slow-regulating discrete devices based on mixed-integer second-order cone programming, while the upper-level model optimizes fast-regulating continuous devices using a multi-agent deep policy gradient algorithm. …”
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Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation by Jin Han, Liang Yang

“…First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). …”
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Exploration design for Q-learning-based adaptive linear quadratic optimal regulators under stochastic disturbances by Vina Putri Virgiani, Shiro Masuda

“…The decaying method gradually reduces the exploration signal over time, enabling effective initial exploration while stabilizing as the system approaches optimal performance. …”
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Optimized Controller Design Using Hybrid Real-Time Model Identification with LSTM-Based Adaptive Control by Yeon-Jeong Park, Joon-Ho Cho

“…The method is improved through a combination of numerical calculation, Genetic Algorithms, and LSTM networks, showing approximately 15% better performance compared to conventional methods. …”
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Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components by Anna Konovalenko, Lars Magnus Hvattum

“…<i>Background:</i> The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. …”
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