Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…A key innovation of this algorithm is its capability to capture critical model knowledge using low-complexity calculations, which helps to further optimize the GNN model. …”
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Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

“…We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. …”
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Orga-Dete: An Improved Lightweight Deep Learning Model for Lung Organoid Detection and Classification by Xuan Huang, Qin Gao, Hanwen Zhang, Fuhong Min, Dong Li, Gangyin Luo

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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction by Madhusmita Das, Biju R. Mohan, Ram Mohana Reddy Guddeti

“…In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. …”
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Model Optimization for High-Yield Biocrude in Co-Hydrothermal Liquefaction of Municipal Sludge by Botian HAO, Yunfei DIAO, Ya WEI, Donghai XU

“…After training with the Levenberg-Marquardt algorithm, the model′s R2 significantly improved to 0.9989, demonstrating the superiority of neural networks in modeling nonlinear complex systems. …”
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A model for placing electric vehicle charging stations in megapolis based on the sparrow search algorithm by Sizhuo Du, D. V. Kapski

“…First, the main factors of placing electric vehicle charging stations are analyzed from different points of view, a multi-purpose model for choosing the address of the charging station is built, an algorithmic model is proposed to improve the sparrow search algorithm as the basis for a specific solution method, and finally, the effect of applying the model and solution method is verified by analyzing examples. …”
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Structural Parameter Identification Using Multi-Objective Modified Directional Bat Algorithm by LIU Li-jun, LIN Ying-hai, SU Yong-hui, LEI Ying

“…This approach improved the accuracy and robustness of structural parameter identification while maintaining computational efficiency.MethodsMOMDBA is an enhanced version of the Directional Bat Algorithm （DBA）， a swarm intelligence optimization technique inspired by the echolocation behavior of bats. …”
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A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network by Zhijie Zhu, Chen Sun, Xicai Gao, Zhuang Liang

“…However, because the prediction effect of the GRNN is influenced by the smoothing factor, the CFOA is adopted to optimize this factor. In this way, influences of human factors during parameter determination of the GRNN prediction model are decreased, and the prediction accuracy and applicability of the model are improved. …”
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An Improved Substation Locating and Sizing Method Based on the Weighted Voronoi Diagram and the Transportation Model by Shiju Wang, Zhiying Lu, Shaoyun Ge, Chengshan Wang

“…Large amount of experiments show that the improved method can get more reasonable and more optimized planning result within shorter time than the original WVD and other algorithms.…”
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Redefining Trauma Triage for Elderly Adults: Development of Age-Specific Guidelines for Improved Patient Outcomes Based on a Machine-Learning Algorithm by Ji Yeon Lim, Yongho Jee, Seong Gyu Choi, Yoon Hee Choi, Sam S. Torbati, Carl T. Berdahl, Sun Hwa Lee

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Inversion of permeability coefficient based on adaptive differential hybrid butterfly particle algorithm by Zhao YANG, Donglin DONG, Yuqi CHEN, Rong WANG

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Logistics Hub and Route Optimization in the Physical Internet Paradigm by Hisatoshi Naganawa, Enna Hirata, Nailah Firdausiyah, Russell G. Thompson

“…<i>Methods:</i> We propose a novel demography-weighted combinatorial optimization model utilizing a genetic algorithm and the Lin–Kernighan heuristic. …”
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Location Optimization Model of a Greenhouse Sensor Based on Multisource Data Fusion by DianJu Qiao, ZhenWei Zhang, FangHao Liu, Bo Sun

“…In the traditional case, the uncertainty of the ambient temperature measured by the experiential distributed sensor is considered. In this paper, a model based on the moving least square method in the fusion algorithm is proposed to study the optimal monitoring point of the sensor in the greenhouse and determine the most suitable installation position of the sensor in the greenhouse to improve the control effect of the temperature control device of the system. …”
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Frequency Dependent Spencer Modeling of Magnetorheological Damper Using Hybrid Optimization Approach by Ali Fellah Jahromi, Rama B. Bhat, Wen-Fang Xie

“…The parameters of the model are identified using an experimental data based hybrid optimization approach which is a combination of Genetic Algorithm and Sequential Quadratic Programming approach. …”
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A New System Reliability Optimization Model Based on Swapping Existing Components by Yuxiong Li, Xianzhen Huang, Xinong En, Pengfei Ding

“…Replacing failed components with other functioning components properly in the original system can be an attractive way for improving system reliability. This paper proposes a new system reliability optimization model to achieve optimal component reliability and the ideal component-swapping strategy under a certain set of constraints. …”
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Research and application of intelligent learning path optimization based on LSTM-Transformer model by Jinling Wang, Wandong Chai

“…Experimental comparison shows that compared with the traditional learning path recommendation algorithm, the optimization strategy based on the LSTM-Transformer model has achieved remarkable results, with the learner's knowledge mastery rate greatly increased from 75 % to 95 %, the learning time shortened by about 25 %, and the learning satisfaction also increased from 70 % to 90 %, which verifies the research hypothesis and fully proves that the LSTM-Transformer model has high application value in intelligent learning path optimization.…”
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Design Optimization of Parameters for Resolver Software Decoding Based on Surrogate Model Management by DENG Yu, LING Yuelun, WU Zuolai, PENG Zaiwu, WANG Jun

“…Finally, a genetic algorithm (GA) was utilized to optimize parameters in the resolver software decoding system through a solving process, and a partial encryption method was introduced to iteratively update the surrogate model, yielding a partially optimal solution that meets the error requirements. …”
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A Hierarchical Path Planning Framework of Plant Protection UAV Based on the Improved D3QN Algorithm and Remote Sensing Image by Haitao Fu, Zheng Li, Jian Lu, Weijian Zhang, Yuxuan Feng, Li Zhu, He Liu, Jian Li

“…To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. …”
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Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance by Ruiqi Yu, Qingdang Li, Jiewei Ji, Tingting Wu, Jian Mao, Shun Liu, Zhen Sun

“…The algorithm enhances the perception and learning capabilities by optimizing the network model and employs a dynamic exploration strategy that encourages exploration in the early stage and reduces it later to accelerate convergence and improve efficiency. …”
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