A sensor node scheduling algorithm for heterogeneous wireless sensor networks by Zhangquan Wang, Yourong Chen, Banteng Liu, Haibo Yang, Ziyi Su, Yunkai Zhu

“…Regional coverage increment optimization model, arc coverage increment optimization model, and residual energy optimization model are proposed. …”
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Adaptive Differential Evolution Algorithm for Forward Position Solution of Parallel Mechanism by Yi Jian

“…According to the constrained length of the bars,the unconstrained optimization model is constructed to formulate the forward position analysis for a 6 degrees-of-freedom general 6-SPS parallel mechanism,and the differential evolution（ DE） algorithm is employed to solve this problem. …”
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Task distribution offloading algorithm of vehicle edge network based on DQN by Haitao ZHAO, Tangwei ZHANG, Yue CHEN, Houlin ZHAO, Hongbo ZHU

“…In order to achieve the best balance between latency,computational rate and energy consumption,for a edge access network of IoV,a distribution offloading algorithm based on deep Q network (DQN) was considered.Firstly,these tasks of different vehicles were prioritized according to the analytic hierarchy process (AHP),so as to give different weights to the task processing rate to establish a relationship model.Secondly,by introducing edge computing based on DQN,the task offloading model was established by making weighted sum of task processing rate as optimization goal,which realized the long-term utility of strategies for offloading decisions.The performance evaluation results show that,compared with the Q-learning algorithm,the average task processing delay of the proposed method can effectively improve the task offload efficiency.…”
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Speech emotion recognition algorithm of intelligent robot based on ACO-SVM by Xueliang Kang

“…The experimental results showed that the average recognition rate of the multilevel SVM model optimized by genetic algorithm was as high as 81.26 %, which was 8.78 percentage points higher than that before optimization. …”
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An improved hybrid approach involving deep learning for urban greening tree species classification with Pléiades Neo 4 imagery—A case study from Nanjing, Eastern China by Min Sun, Stephane G.P. Debulois, Zhengnan Zhang, Xiaolei Cui, Zhili Chen, Mingshi Li

“…Future work will integrate multi-source data, multi-seasonal observations, and adaptive algorithms to further enhance classification performance and improve model robustness across diverse urban environments.…”
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Optimum problem of piezoelectric laminated composite plate using genetic algorithm by Le Kim Ngoc, Tran Ich Thinh

“…A nine-node isoparametric rectangular element with 5 degrees of freedom for the generalized displacements and 2 electrical degrees of freedom at each node is used. Optimization techniques based on genetic algorithm (GAs) are applied in order to maximize the piezoelectric actuator efficiency, improve the structural performance. …”
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Estimating forest aboveground carbon sink based on landsat time series and its response to climate change by Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang

“…In this study, a genetic algorithm (GA) was used to optimize the parameters of random forest (RF) to establish dynamic models to estimate the carbon sink intensity (CSI) of Pinus densata in Shangri-La and analyze the combined effects of multi-climatic factors on CSI. …”
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Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching by LI Cheng-yan, ZHAO Shuai, CHE Zi-xuan

“…In order to improve the detection accuracy of SSD (Single Shot MultiBox Detector), an A-SSD (Anchor-object SSD) object detection algorithm based on Anchor-object matching is proposed. …”
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Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting by Zihan CHEN, Wei TENG, Xuefeng XU, Xian DING, Yibing LIU

“…In order to make full use of the prior relationships among data features and improve the prediction accuracy of medium and long term wind power at wind farms, a medium and long term wind power prediction model based on graph convolution neural network (GCN), wind velocity differential fitting (DF), and particle swarm optimization (PSO) is proposed. …”
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Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems by Yadong Yu, Haiping Ma, Mei Yu, Sengang Ye, Xiaolei Chen

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EPRSA: interference resource scheduling algorithms for air-ground communication networks by Wenpeng Wu, Zhenhua Wei, Haiyang You, Xiaoqing Li, Jianwei Zhan, Zhaoguang Zhang

“…Based on this, this paper proposes the interference link margin in the mathematical model and normalizes and quantifies the optimization objectives. …”
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PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN by Na ZHANG, Qiang REN, Guangchen LIU, Liping GUO, Jingyu LI

“…Considering the blindness and randomness of weights and thresholds of traditional Elman neural networks and the fluctuation and nonstationarity of PV output power signal, the paper proposes a short-term prediction model of PV output power based on variational mode decomposition (VMD) and an Elman neural network optimized by grey wolf optimization (GWO) algorithm. …”
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Assessing and Reducing Energy Consumption of Grain Cleaning and Drying Equipment by Yu. N. Sap’yan, V. A. Kolos, M. I. Suleymanov, E. N. Kabakova

“…(Research purpose) To adapt the criteria and algorithm of energy-saving optimization of field technology to assess and improve the status of energy efficiency, reduce energy consumption and identify the maximum available energy saving potential of grain drying equipment during energy audit. …”
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EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm by Younkyung Jwa, Chang Wook Ahn, Man-Je Kim

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Optimal allocation of renewable energy resources in a smart grid under neutrosophic credibility-based uncertainty by Muhammad Zafar Abbas, Rukhshanda Anjum, Muhammad Umar Mirza, Abdulrahman A. Almehizia, Amir Hussain

“…Based on NCNs, we introduce a novel aggregation operator termed as generalized neutrosophic fuzzy credibility numbers weighted averaging (GNFCNWA) operator which enhances aggregation of uncertain information and improves the credibility of decisions. Furthermore, we also provide an algorithm that extends the utilization of NCNs with the GNFCNWA operator in a complete approach to decision-making. …”
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IDE-SVM algorithm and it's usage in physical layer security method for IoT by WANG Qiang, ZHU Chenming, PAN Su, QIN Yuxi

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Precise wind allocation scheme decision based on attraction-repulsion algorithm by NI Jingfeng, CHEN Dunwei, LIU Yujiao

“…Performance test results showed that AROA had a significant advantage in comprehensive optimization performance compared to Genetic Algorithm (GA), Simulated Annealing-Improved Particle Swarm Optimization (SA-IPSO), and Monotonic Basin Hopping (MBH). …”
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Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets by Ali Keyvandarian, Ahmed Saif, Ronald Pelot

“…Simulation reveals a 10.7% increase in capital cost on average but a 36.2% decrease in operational cost, resulting in a 16.4% total cost reduction and an 8.1% improvement in system reliability compared to the nominal model employing point estimates. …”
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Algorithm of underdetermined convolutive blind source separation for high reverberation environment by Yuan XIE, Tao1 ZOU, Weijun SUN, Shengli XIE

“…To separate the underdetermined convolutive mixture signals in the high reverberation environment, a novel algorithm of underdetermined convolutive blind source separation was proposed.Aiming at the influence of high reverberation environment, a global impulse response network was designed to weaken reverberation echo, improving signal quality.A new mathematical model of time-frequency mixing signals was established based on the global impulse response network.The global impulse response matrix which shortened the length of the traditional impulse response, reduced the approximation error of model transformation caused by high reverberation.The real-time update learning rules of model parameters were designed based on the theory of nonnegative matrix factorization, and the source signal separation problem was converted into the model parameter optimization problem, achieving blind source separation of mixing signals.Experimental results show that the proposed algorithm can effectively realize the blind source separation of Chinese and English speech and music signals, and the comparision with existing popular algorithms verified the superiority of the proposed algorithm.…”
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Research on floating object classification algorithm based on convolutional neural network by Jikai Yang, Zihan Li, Ziyan Gu, Wei Li

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