Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors by Yueyou Tang, Anfu Zhang, Qi Zhou, Mu He, Liang Xia

“…To overcome these limitations, present work proposes a physics–data collaborative design framework that integrates nonlinear topology optimization with neural networks. This framework first generates baseline configuration approximating the target mechanical behavior via nonlinear topology optimization, thereby establishing a physically reliable initial design space. …”
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Three-Layer Framework Integrating Optimal Placement of Supervisory, Control, and Acquisition System Measurements with Clustering-Based Electric Substations Selection for State Esti... by Vasilica Dandea, Stefania Galbau, Mihai-Alexandru Baciu, Gheorghe Grigoras

“…One of the biggest challenges, both from a technical and economic point of view, of the Distribution Network Operators refers to identifying the locations (electric distribution substations) integrated into a supervisory, control, and acquisition (SCADA) system to perform on-site measurements used in the state estimation of the electric distribution networks (EDNs). …”
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Adaptive Channel Division and Subchannel Allocation for Orthogonal Frequency Division Multiple Access-Based Airborne Power Line Communication Networks by Ruowen Yan, Qiao Li, Huagang Xiong

“…We introduce pioneering algorithms for channel division and subchannel allocation within Orthogonal Frequency Division Multiple Access (OFDMA)-based airborne PLC networks, aimed at optimizing network performance in key areas such as throughput, average delay, and fairness. …”
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A green hydrogen production model from solar powered water electrolyze based on deep chaotic Lévy gazelle optimization by Heba Askr, Mahmoud Abdel-Salam, Václav Snášel, Aboul Ella Hassanien

“…DeepGaz combines a new Chaotic-Lévy variant of the gazelle optimization algorithm (CGOA) with a recurrent neural network (RNN/LSTM) for hyperparameter optimization. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. …”
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LEO computing satellite constellation design for heterogeneous QoS requirements by PENG Yuming, SUN Yijing, DI Boya

“…By establishing a satellite-to-terrestrial connection model, the average computational resources and backhaul capacity available to ground users were analyzed and a multi-objective optimization problem for designing ultra-dense LEO constellations was modelled, aiming to minimize the total number of satellites while meeting the heterogeneous quality of service (QoS) requirements for user task offloading. …”
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NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters by Peng Li, Chen Qing, Hao Liu

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Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma, Shuangzhe Liu

“…To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). …”
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Optimizing sustainable blended concrete mixes using deep learning and multi-objective optimization by Rupesh Kumar Tipu, Preeti Rathi, Kartik S. Pandya, Vijay R. Panchal

“…The Bayesian hyperparameter tuning technique produces an optimal network configuration which delivers an average $$R^2$$ of 0.936 together with an RMSE of 5.71 MPa during 5-fold cross-validation. …”
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Prediction Analysis of Pre-Camber for Continuous Girder Bridge Cantilever Casting Construction Based on DBO-CNN-BiLSTM-Attention Neural Network by Jinyang Zhang, Haiqing Liu, Xiangen Gong, Ming Lei, Zimu Chen

“…Therefore, this paper proposes a pre-camber prediction model based on a Convolutional-Bidirectional Long Short-Term Memory network with a fusion attention mechanism (CNN-BiLSTM-Attention) and utilizes the Dung Beetle Optimizer (DBO) algorithm to optimize the hyperparameters of the CNN-BiLSTM-Attention model to enhance its predictive performance. …”
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Design and verification of the onboard 6G core network architecture and network functions by WANG Shangguang, WANG Chao, MA Xiao, XING Ruolin, ZHOU Ao

“…The network elements of the onboard 6G core network were optimized, including access and mobility management, session management, distributed service registration and discovery, etc. …”
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High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators by K. Dautov, G. Tolebi, M. S. Hashmi, A. Jarndal, E. Almajali, G. Nauryzbayev

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Optimal policy for composite sensing with crowdsourcing by Bei Zhao, Siwen Zheng, Jianhui Zhang

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OPTIMAL ROUTING: QUALITY OF SERVICE by N. I. Listopad, A. A. Hayder

“…The general principals of optimal routing are formulated. The average packet delay in the network is analyzed more detail. …”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Secure Cooperative Dual-RIS-Aided V2V Communication: An Evolutionary Transformer–GRU Framework for Secrecy Rate Maximization in Vehicular Networks by Elnaz Bashir, Francisco Hernando-Gallego, Diego Martín, Farzaneh Shoushtari

“…To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. …”
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Joint Sampling and Transmission Policies for Minimizing Cost Under Age of Information Constraints by Emmanouil Fountoulakis, Marian Codreanu, Anthony Ephremides, Nikolaos Pappas

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Emotion recognition with a Randomized CNN-multihead-attention hybrid model optimized by evolutionary intelligence algorithm by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Filippo Sanfilippo

“…To address these challenges, we propose an innovative emotion recognition framework that integrates a Randomised Convolutional Neural Network (RCNN) with a Multi-Head Attention model, further optimized by the Football Team Training Algorithm (FTTA) metaheuristic to enhance network parameters effectively. …”
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Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT Networks by Jihyung Kim, Yeji Cho, Junghyun Kim

“…We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. …”
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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

“…This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. …”
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