Genetic algorithm solving the scheduling problem of large-scale power grid material quality inspection bases under multiple constraints by Li Lin, Chen Yongqiang

“…Based on the characteristics of the problem, a genetic algorithm is proposed as a tool for solving, and the encoding and decoding in the algorithm are specially designed for the detection base to ensure the generation of feasible solutions; Introducing roulette wheel to select populations, combining the characteristics of the problem to randomly mutate chromosomes, and using POX for chromosome crossover, in order to obtain the optimal solution to the problem. …”
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Design of intelligent optimization of sports strategy and training decision support system based on deep reinforcement learning by Hua Xu, Bing Lin, Long Liu

“…The data is preprocessed by a sliding window average filter algorithm to eliminate noise and outliers. The system adopts the DQN (Deep Q-Network) architecture and applies dual DQN technology to improve model stability. …”
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Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. …”
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Three-Dimensional Aerodynamic Optimization of Single-Layer Reticulated Cylindrical Roofs Subjected to Mean Wind Loads by Bingbing San, Chen Xu, Ye Qiu

“…This paper presents a procedure to optimize the rise-to-span ratio of single-layer reticulated cylindrical roofs to improve their aerodynamic performance, by coupling the optimization method with computational fluid dynamics (CFD) and finite element analysis (FEA). …”
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Optimizing coverage in wireless sensor networks using deep reinforcement learning with graph neural networks by G. Pushpa, R. Anand Babu, S. Subashree, S. Senthilkumar

“…Traditional optimization techniques, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have demonstrated adaptability in node placement but struggle with real-time self-learning capabilities, requiring frequent retraining to handle continuously changing conditions. …”
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Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM by Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea

“…Results: Multiple rounds of empirical experiments using different sets of optimizers and configurations, supported by the visualization capabilities of TensorBoard, demonstrate significant improvements in the performance of the AAO-BiLSTM model. …”
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Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT) by Hossam Hassan Ammar, Ahmad Taher Azar, Raafat Shalaby, M. I. Mahmoud

“…This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. …”
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A hybrid recommendation system for Chinese character components using a dual-tower neural network and a deterministic algorithm by Shaoqing Yang

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ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions by Pavan Kumar S, Nirav Pravinbhai Bhatt

“…We present ThermOptCOBRA, a comprehensive solution consisting of four algorithms for optimal model construction and analysis that integrate thermodynamic constraints to address TICs. …”
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A Survey on Video Compression Optimization Techniques for Accuracy Enhancement in Video Analytics Applications (VAPs) by Kholidiyah Masykuroh, Hendrawan, Eueung Mulyana, Farhan Krishna

“…We examine traditional compression algorithms, machine learning-based approaches, dynamic parameter adjustment strategies, and hybrid models, each offering unique strengths and limitations. …”
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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

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Optimizing Joint Decisions of Dynamic Pricing and Ticket Allocation for High-Speed Railway with Operators’ Risk Preference by Huaqin Meng, Zhenying Yan, Yu Wang, Yiwei Xu

“…Then, the combination algorithm of particle swarm optimization algorithm and mixed-integer linear programming was designed to solve the model. …”
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Optimizing resource allocation in remote healthcare via blockchain-enabled decentralized networks and spectral clustering by A. Kanimozhi, K. Vidya

“…The proposed system utilizes the InterPlanetary File System (IPFS) to handle resource requests transparently and securely, while spectral and agglomerative clustering algorithms are employed to optimize delivery routes. …”
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Enhancing Power Efficiency in 4IR Solar Plants through AI-Powered Energy Optimization by S. Boobalan, TR. Kalai Lakshmi, Shubhangi N. Ghate, Mohammed Hameeduddin Haqqani, Sushma Jaiswal

“…The AI-powered system relies on intelligent algorithms to identify the most efficient energy sources for the industry’s needs and adjust them accordingly while learning from every task it is given. …”
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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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Research on Multi-Objective Optimization Design of High-Speed Train Wheel Profile Based on RPSTC-GJO by Mao Li, Hao Ding, Meiqi Wang, Xingda Yang, Bin Kong

“…Secondly, the improved multi-objective optimization algorithm and data-driven model are used to optimize the tread coordinates and obtain an optimized profile. …”
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Reactive power optimization via deep transfer reinforcement learning for efficient adaptation to multiple scenarios by Congbo Bi, Di Liu, Lipeng Zhu, Chao Lu, Shiyang Li, Yingqi Tang

“…Existing reinforcement learning algorithms alleviate the computational complexity in optimization but suffer from the inefficiency of model retraining for different operating scenarios. …”
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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

“…Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. …”
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Coordinated Scheduling of Distribution Networks and Microgrids Considering Heat Pump Load and Distributed Photovoltaic by Shihui LI, Qi WANG, Xiaobo JIA, Wenchuan WU, Huayang LIANG, Lin ZHU

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Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers by Anupam Yadav, Rifat Hussain, Madhu Shukla, Jayaprakash B, Rishiv Kalia, S. Prince Mary, Chou-Yi Hsu, Manoj Kumar Mishra, Kashif Saleem, Mohammed El-Meligy

“…Meta-heuristic algorithms offer an alternative to traditional gradient-based optimizers for improving DNNs performance. …”
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