Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury by Yukun Wang, Xuebo Chen

“…Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. …”
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Optimizing sum rates in IoT networks: A novel IRS-NOMA cooperative system by Mukkara Prasanna Kumar, Ammar Summaq, Sunil Chinnadurai, Poongundran Selvaprabhu, Vinoth Babu Kumaravelu, Md. Abdul Latif Sarker, Dong Seog Han

“…By leveraging our optimization algorithm, the proposed system ensures efficient resource allocation, achieving superior spectral efficiency and fairness among users compared to traditional models. …”
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Optimization of the Canopy Three-Dimensional Reconstruction Method for Intercropped Soybeans and Early Yield Prediction by Xiuni Li, Menggen Chen, Shuyuan He, Xiangyao Xu, Panxia Shao, Yahan Su, Lingxiao He, Jia Qiao, Mei Xu, Yao Zhao, Wenyu Yang, Wouter H. Maes, Weiguo Liu

“…With these improvements, a high-precision 3D canopy point-cloud model of soybeans covering the entire growth period was successfully constructed. …”
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Bionic Compass Method Based on Atmospheric Polarization Optimization in Non-Ideal Clear Condition by Yuyang Li, Xia Wang, Min Zhang, Ruiqiang Li, Qiyang Sun

“…This paper proposes a bionic navigation method based on atmospheric polarization optimization to improve heading accuracy under non-ideal clear conditions. …”
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A power-distribution joint optimization arrangement for multi-point source jamming system by Zhihao Cai, Shiqi Xing, Sinong Quan, Xinyuan Su, Junpeng Wang

“…Third, a multi-objective time-varying mapping particle swarm optimization (MOTPSO) algorithm is adopted to solve the model, thereby achieving the optimal arrangement. …”
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Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model by Yu Zhao, Zhenrui Peng

“…To further improve the generalization ability, the input weights and biases of ELM are optimized by Lévy flight trajectory-based whale optimization algorithm (LWOA). …”
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Optimizing urban energy flows: Integrative vehicle-to-building strategies and renewable energy management by Jian Hern Yeoh, Kai-Yun Lo, I-Yun Lisa Hsieh

“…This study introduces a scalable, multi-objective optimization model that utilizes Vehicle-to-Building (V2B) interactions to mitigate grid load by leveraging EVs as mobile energy storage units. …”
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Servo System Control of Turntable Lipstick-Filling Machine Based on Particle Swarm Optimization by Xiangqiang Zhong, Wenbo Tao, Lingling Zou, Huaian Yang, Shunyu Yao

“…The experimental results demonstrate that the qualified rate of lipstick filling, under the particle swarm optimization PID control implementation, is 97%, indicating that the particle swarm algorithm is quite applicable and has an economic impact by improving the accuracy and stability of the lipstick-filling servo system.…”
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Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework by Wenxia Yuan, Lingfang Lan, Jiayi Xu, Tingting Sun, Xinghua Wang, Qiaomei Wang, Jingnan Hu, Baijuan Wang

“…Additionally, Shape Weights and Scale Adjustment Factors are introduced to optimize the loss function. The experimental results show that compared with the original YOLOv10, the model generated by the improved algorithm improves the accuracy by 5.88 percentage points, the recall rate by 6.67 percentage points, the balance score by 6.27 percentage points, the mAP value by 4.26 percentage points, the bounding box loss by 18.75%, the classification loss by 27.27%, and the feature point loss by 8%. …”
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Multi objective optimization and experimental investigation of the stirring performance of a novel micro actuator by Zhuowei He, Junjie Lei, Jingjing Yang, Huba Zhu

“…Subsequently, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is utilized for Multi-Objective Optimization to identify the optimal combination of structural parameters. …”
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Distributed energy storage hierarchical partition dispatch control of virtual power plant based on SaDE-BBO algorithm by Tianyi Yu, Shijia Wei, Tao Lu, Zhipeng Zhang, Ning Sun

“…This method is based on the operation structure of the virtual power plant, analyzes the operating characteristics of the distributed energy storage system and the output of uncertainty factors, considers the grid load, renewable energy and distributed energy storage on the time scale, and constructs hierarchical partitions of the virtual power plant. The dispatch model determines the day-ahead and day-in-day hierarchical partition dispatch control objective functions, and sets corresponding constraints; the dispatch control model based on the solution of the SaDE-BBO algorithm outputs the virtual power plant distributed energy storage hierarchical partition dispatch control optimization plan. …”
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DS-HPPO Deep Reinforcement Learning for Optimal DVFS Control on an Image Signal Processor by Yang Du, Wenhong Li, Xiaoyang Zeng

“…We apply the Parameterized Action Markov Decision Process (PAMDP) to model the above optimization problem and solve the PAMDP with the proposed DS-HPPO deep reinforcement learning algorithm. …”
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Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning by Yizhe Wang, Xiaoguang Yang, Yangdong Liu, Hailun Liang

“…Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve the current signal timing scheme. …”
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Speed and Path Planning Optimization of Autonomous Vehicle to Minimize Lap Time in Racing Track by Young-Jin Roh, Ji-Ung Im, Jong-Hoon Won

“…This method addresses the challenges inherent in high-speed racing environments, where tire friction and vehicle dynamics are critical for achieving optimal lap times. A friction circle model was used to determine the friction coefficient, which is subsequently applied to calculate the maximum feasible speed and acceleration constraints for different track segments. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…This paper presents a reinforcement learning-based approach to automate the tuning of a 6thorder combline bandpass filter, operating at 941 MHz, using a Q-learning algorithm. To reduce complexity, only two tuning screws are considered in the optimization. …”
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Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis by Aliyu Tetengi Ibrahim, Ibrahim Hayatu Hassan, Mohammed Abdullahi, Armand Florentin Donfack Kana, Amina Hassan Abubakar, Mohammed Tukur Mohammed, Lubna A. Gabralla, Mohamad Khoiru Rusydi, Haruna Chiroma

“…The methodology is built on a combination of preprocessing techniques, transfer learning architecture reconstruction, and dynamic fine-tuning strategies. To optimize key hyper-parameters, this study employed the Dynamic Chaotic Whale Optimization Algorithm. …”
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Dual-robot formation transport in random environment and narrow restricted area via improved DDPG navigation by Liang Tang, Ronggeng Ma, Bowen Chen, Yisen Niu

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Intelligent control method of robot based on angle of attack feedback for energy efficiency optimization by WANG Tianzhu, LIU Jiayin, HAN Song, CHE Honglei, SHI Congling

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Optimization Study of PVT Coupled Water Loop Heat Pump Energy Supply System by Lv Tiangang, Liu Bing, Wu Jun, Zhu Li, Hou Jingxuan

“…This study designs a building energy supply system integrating PVT and a water-loop heat pump based on Tianjin's meteorological data, using TRNSYS software to establish a dynamic model. By employing the Hooke-Jeeves algorithm, the system's design and operation parameters were optimized with annual cost and system COP as objective functions. …”
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Deep neural network based distribution system state estimation using hyperparameter optimization by Gergő Békési, Lilla Barancsuk, Bálint Hartmann

“…In particular, it demonstrates the use of the Tree-structured Parzen Estimator algorithm, which is a Bayesian hyperparameter optimization method, for distribution system state estimation on real Hungarian low-voltage networks. …”
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