Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China by Yanna Gao, Hong Dong, Liujun Hu, Fanhong Zeng, Yuqun Gao, Zhuonan Huang, Shaohua Wang, Shaohua Wang

“…To solve this model at large scale, a deep reinforcement learning (DRL) algorithm is designed and implemented. The DRL approach is benchmarked against a traditional optimization implementation using the Gurobi solver. …”
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Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches by Fankun Meng, Jia Liu, Gang Tong, Hui Zhao, Chengyue Wen, Yuhui Zhou, Vamegh Rasouli, Minou Rabiei

“…Therefore, a hybrid optimization framework was designed that combines artificial intelligence methods (Support Vector Regression with the Gaussian kernel, Gaussian-SVR or Long Short-Term Memory, LSTM) and multi-objective optimization algorithms (multiple objective particle swarm optimization, MOPSO or Non-dominated Sorting Genetic Algorithm II, NSGA-II) to find the optimal CO2 injection and production strategies under different water cut. …”
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Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning by Xue Feng, Chi Xu, Xi Jin, Changqing Xia, Jing Jiang

“…To address the end-edge computation offloading challenge in the multi-terminal and multi-server environment, this paper proposes an intelligent computation offloading algorithm based on Lyapunov optimization and deep reinforcement learning. …”
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A DDQN-Guided Dual-Population Evolutionary Multitasking Framework for Constrained Multi-Objective Ship Berthing by Jinyou Mou, Qidan Zhu

“…Simulations on the CSAD vessel model demonstrate that this framework outperforms baseline algorithms such as evolutionary multitasking constrained multi-objective optimization (EMCMO), DQN, Q-learning, and non-dominated sorting genetic algorithm II (NSGA-II), achieving superior efficiency and stability while maintaining the required berthing angle. …”
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Applied AMT machine learning and multi-objective optimization for enhanced performance and reduced environmental impact of sunflower oil biodiesel in compression ignition engine by Ali A. Al-jabiri, Hyder H. Balla, Mudhaffar S. Al-zuhairy, Hussein Alahmer, Ahmed Al-Manea, Raed Al-Rbaihat, Ali Alahmer

“…Additionally, the study employs a multi-objective ameliorative whale optimization algorithm (AWOA) to optimize the model's output. …”
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Optimization of microwave-assisted polyphenol extraction and antioxidant activity from papaya peel using response surface methodology and artificial neural network by Md. Waziur Rahman Chy, Tanvir Ahmed, Junaid Iftekhar, Md. Zohurul Islam, Md. Rahmatuzzaman Rana

“…These models were combined with the desirability function (DF) and/or genetic algorithm (GA) optimization approaches. Maximizing TPC and DPPH activity while maintaining MWP, I-time, EtOH%, and S/S within their respective ranges using hybrid optimization approaches (RSM-DF: TPC = 1058 mgGAE/100 g, and DPPH = 83 %, RSM-GA: TPC = 1064 mgGAE/100 g and DPPH = 79 %, and ANN-GA: TPC = 1086 mgGAE/100 g, and DPPH = 83 %,) yielded consistent optimal results. …”
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Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning by Karim Maghfour Sarkarabad, Mohsen Shayanmehr, Ahad Ghaemi

“…We employed radial basis function (RBF) and multilayer perceptron (MLP) algorithms for ANN modeling. The optimal MLP configuration, utilizing the Levenberg–Marquardt (Trainlm) algorithm, consisted of three hidden layers with 20, 17, and 9 neurons, respectively, while the optimal RBF network contained 43 neurons. …”
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Bilevel Optimization Framework for Multiregional Integrated Energy Systems Considering 6G Network Slicing and Battery Energy Storage Capacity Sharing by Kun Cui, Ming Chi, Yong Zhao, Zhi-Wei Liu

“…The upper-level model maximizes the profit of generation units by optimizing their bidding strategies, while the lower-level model aims to maximize social welfare through market clearing. …”
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A novel hybrid fruit fly and simulated annealing optimized faster R-CNN for detection and classification of tomato plant leaf diseases by E. Gangadevi, Ben Othman Soufiane, Balamurugan Balusamy, Firoz Khan, Masresha Getahun

“…By hybridizing the fruit fly optimization algorithm and simulated annealing, the Faster R-CNN’s hyper-parameter issues are addressed, and the convergence rate is improved. …”
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Optimized frequency stabilization in hybrid renewable power grids with integrated energy storage systems using a modified fuzzy-TID controller by Mohamed Khamies, Ahmed H. A. Elkasem, Salah Kamel, Mohamed Hashem

“…The parameters of the strategies are optimized using a recent metaheuristic algorithm known as the Sea Horse Optimizer (SHO) under different operating conditions. …”
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An improved lightweight tiny-person detection network based on YOLOv8: IYFVMNet by Fan Yang, Lihu Pan, Hongyan Cui, Linliang Zhang

“…This operation also reduces the computational cost by decreasing the amount of required feature map channels, while maintaining the effectiveness of the feature representation. (3) he Minimum Point Distance Intersection over Union loss function is employed to optimize bounding box detection during model training. (4) to construct the overall network structure, the Layer-wise Adaptive Momentum Pruning algorithm is used for thinning.ResultsExperiments on the TinyPerson dataset demonstrate that IYFVMNet achieves a 46.3% precision, 30% recall, 29.3% mAP50, and 11.8% mAP50-95.DiscussionThe model exhibits higher performance in terms of accuracy and efficiency when compared to other benchmark models, which demonstrates the effectiveness of the improved algorithm (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) in small-object detection and provides a reference for future research.…”
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Deployment scheme of RSU based on connection time in VANET by Zhengchao DING, Zhenchun WEI, Lin FENG

“…For the roadside unit (RSU) placement problem in vehicular Ad Hoc network (VANET),the deployment scheme of RSU based on connection time was proposed.The scheme find the optimal positions of RSU for maximizing the number of vehicles while ensuring a certain level of connection time under the limited number of RSU.The problem was modeled as a maximum coverage problem,and a binary particle swarm algorithm was designed to solve it.The simulation experiment was carried out with the real Beijing road network map and taxi GPS data.The simulation results show that the algorithm is convergent,stable and feasible.Compared with the greedy algorithm,the proposed scheme can provide continuous network service for more vehicles.…”
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Deployment scheme of RSU based on connection time in VANET by Zhengchao DING, Zhenchun WEI, Lin FENG

“…For the roadside unit (RSU) placement problem in vehicular Ad Hoc network (VANET),the deployment scheme of RSU based on connection time was proposed.The scheme find the optimal positions of RSU for maximizing the number of vehicles while ensuring a certain level of connection time under the limited number of RSU.The problem was modeled as a maximum coverage problem,and a binary particle swarm algorithm was designed to solve it.The simulation experiment was carried out with the real Beijing road network map and taxi GPS data.The simulation results show that the algorithm is convergent,stable and feasible.Compared with the greedy algorithm,the proposed scheme can provide continuous network service for more vehicles.…”
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Research on Impact of Planned Path Length and Yaw Cost on Collaborative Search of Unmanned Aerial Vehicle Swarms by Heng Zhang, Wenyue Meng, Yanan Liu, Guanyu Liu, Jian Zhang

“…To address the unclear impacts of a planned path length and yaw cost on search performance in large-scale Unmanned Aerial Vehicle (UAV) swarm collaborative search scenarios under complex and dynamic environments, a path grid determination algorithm is proposed, transforming the path-planning problem into an optimal waypoint selection problem, enabling UAVs to make rapid decisions using the Particle Swarm Optimization (PSO) algorithm. …”
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A Hybrid GRA-TOPSIS-RFR Optimization Approach for Minimizing Burrs in Micro-Milling of Ti-6Al-4V Alloys by Rongkai Tan, Abhilash Puthanveettil Madathil, Qi Liu, Jian Cheng, Fengtao Lin

“…However, its practical applications are hindered by significant challenges, particularly the unavoidable generation of burrs, which complicate subsequent finishing processes and adversely affect overall part quality. To optimize the burr formation in the micro-milling of Ti-6Al-4V alloys, this study proposes a novel hybrid-ranking optimization algorithm that integrates Grey Relational Analysis (GRA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. …”
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…The first stage employs an optimized increment-based detection algorithm achieving 95.0% for recall and 54.8% for precision through multidimensional analysis. …”
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Advanced Solar Irradiance Forecasting Using Hybrid Ensemble Deep Learning and Multisite Data Analytics for Optimal Solar-Hydro Hybrid Power Plants by Sudharshan Konduru, Naveen C., Jagabar Sathik M.

“…A novel hybrid decomposed residual ensembling model for deep learning (SBLTSRARW) using models such as autoregressive integrated moving average (ARIMA) and seasonal-trend decomposition using loess (STL) along with prediction and optimization models such as Bidirectional LSTM (Bi-LSTM), and Whale Optimization Algorithm (WOA) methods are used to predict the irradiances. …”
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Uncertainty management in multiobjective electric vehicle integrated optimal power flow based hydrothermal scheduling of renewable power system for environmental sustainability by Susanta Dutta, Siddhartha Ghosh, Tushnik Sarkar, Provas Kumar Roy, Chandan Paul, Ruba Abu Khurma, Mohd Asif Shah, Saurav Mallik

“…Secondly, renewable energy sources such as wind-solar-EV are integrated with the aforesaid systems for lowering fuel cost, emission, active power loss (APL), aggregated voltage deviation (AVD), voltage stability index (VSI) and also cost, emision, APL, AVD, VSI are reduced simultaneously considering different cases for multi-objective functions.Proposed sine-cosine algorithm (SCA) embedded with quasi-oppositional based learning (QOBL), known as QOSCA is used to balance the exploration and exploitation ability in order to overcome shortcomings and provide global optimal solutions. …”
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Max–Min Secrecy Rate and Secrecy Energy Efficiency Optimization for RIS-Aided VLC Systems: RSMA Versus NOMA by Omar Maraqa, Sylvester Aboagye, Majid H. Khoshafa, Telex M. N. Ngatched

“…This paper presents a comprehensive study on the joint optimization of VLC access point (AP) power allocation, RIS association, and RIS elements orientation angles for secure VLC systems, while considering rate-splitting multiple access (RSMA) and power-domain non-orthogonal multiple access (NOMA) schemes. …”
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