Deep Reinforcement Learning-Based Two-Phase Hybrid Optimization for Scheduling Agile Earth Observation Satellites by Guanghui Zhou, Zhicheng Jin, Dongning Liu

“…The experimental results demonstrate that the TPHO framework with MRC rules achieves superior performance, yielding a total reward improvement exceeding 16% compared with the A-ALNS algorithm in the most complex scenario involving 1200 tasks, yet requiring less than 3% of the computational duration of A-ALNS.…”
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Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification by Amala Mary Vincent, P. Jidesh, A. A. Bini

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A novel feature selection algorithm using decomposition based multi-objective guided honey badger algorithm (MO-GHBA) and NSGA-III by Anusha Papasani, Nagaraju Devarakonda

“…In most of the MOEAs based feature selection algorithms, more optimal solutions are obtained around the Pareto front's center because of the deficiency in selection features. …”
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Optimizing laser powder bed fusion parameters for enhanced hardness of Ti6Al4V alloys: A comparative analysis of metaheuristic algorithms for process parameter optimization by Praveenkumar V, Vijaykumar S. Jatti, Saiyathibrahim A, Praveen Kumar D, Murali Krishnan R, Vinaykumar S. Jatti, A. Johnson Santhosh

“…Given its simplicity alongside its accuracy and robust performance, the JAYA algorithm proves the most appropriate method for LPBF parameter optimization. …”
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Optimization of a Coupled Neuron Model Based on Deep Reinforcement Learning and Application of the Model in Bearing Fault Diagnosis by Shan Wang, Jiaxiang Li, Xinsheng Xu, Ruiqi Wu, Yuhang Qiu, Xuwen Chen, Zijian Qiao

“…By comparing the coupled neuron model optimized with a reinforcement learning algorithm, particle swarm algorithm, and quantum particle swarm algorithm, the experimental results show that the coupled neuron model optimized with a deep reinforcement learning algorithm has the optimal signal-to-noise ratio of the output signal and recognition rate of the bearing faults, which are −13.0407 dB and 100%, respectively. …”
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Leveraging prior mean models for faster Bayesian optimization of particle accelerators by Tobias Boltz, Jose L. Martinez, Connie Xu, Kathryn R. L. Baker, Zihan Zhu, Jenny Morgan, Ryan Roussel, Daniel Ratner, Brahim Mustapha, Auralee L. Edelen

“…Abstract Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. …”
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Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

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Research on cutting mechanism and process optimization method of gear skiving by Peng Wang, Yuanchao Ni, Xiaoqiang Wu, Jiaxue Ji, Geng Li, Jiahao Wu

“…Furthermore, a prediction model of cutting force and cutting temperature is established using a neural network optimized by genetic algorithm. …”
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Numerical modeling and neural network optimization for advanced solar panel efficiency by Udit Mamodiya, Indra Kishor, Mohammed Amin Almaiah, Monia Hamdi, Rami Shehab, Tayseer Alkhdour

“…Conventionally, such optimization techniques—MPPT (Maximum Power Point Tracking) along with heuristic algorithms—suffer significantly from slow adaptability and track sub optimality under dynamic environments. …”
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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

“…The HiGMA algorithm plays a crucial role in identifying and extracting the most relevant regions of affected skin through optimal segmentation. …”
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Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems by Abubakar Unguwanrimi Yakubu, Liu Qingsheng, Meng Kai, Chen Jinwei, Omer Abbaker Ahmed Mohammed, Jiahao Zhao, Qi Jiang, Xuanhong Ye, Junyi Liu, Qinglong Yu, Muhammad Aurangzeb, Shusheng Xiong

“…Optimization algorithms such as PSO, WOA, MIGA, and NSGA-II have shown promising results, including up to 15 % reduction in hydrogen consumption and 20 to 30 % improvement in thermal uniformity. …”
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A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms by Yuan Zhang, Zhekui Fan, Wenjia Yan, Chentian Ge, Huasheng Sun

“…With the optimal model, three red-edge bands of Landsat OLI were subsequently obtained in alignment with their derived vegetation indices. …”
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Research on the method of weight calculation and equipment arrangement optimization of tramcar by DUAN Huadong, LIU Xiaofeng, JIANG Zhongcheng, ZHANG Bo

“…Based on Isight optimization platform, multi-objective optimization algorithm was adopted to improve the equipment layout. …”
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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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An Investigation into the Rescue-Path Planning Algorithm for Multiple Mine Rescue Teams Based on FA-MDPSO and an Improved Force-Directed Layout by Qiangyu Zheng, Peijiang Ding, Zhixin Qin, Zhenguo Yan

“…Subsequently, the hyperparameters of MDPSO (Multiple Constraints Discrete Particle Swarm Optimisation) were optimised by means of four intelligent algorithms—ACO (Ant Colony Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and WOA (Whale Optimization Algorithm). …”
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Synergistic integration of refined pelican optimization algorithm and deep neural networks for autonomous vehicle control in edge computing architectures by Fude Duan, Bing Han, Xiongzhu Bu

“…The chief contributions of the present study have been threefold: (1) the improvement of a particular autonomous driving method optimized for mobile edge computing platforms; (2) the arrangement of an optimized MobileNet method employing the RPO algorithm that uses LiDAR sensor data for effective object recognition and path design; and (3) the construction of an indoor vehicle prototype by mean of a microcontroller and LiDAR sensors, after a comprehensive performance evaluation of inference models, and analyzing the trade-offs between input size and computational effectiveness. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.…”
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Research on Self-Recovery Ignition Protection Circuit for High-Voltage Power Supply System Based on Improved Gray Wolf Algorithm by Jingyi Zhu, Wanlu Zhu, Haifeng Wei, Yi Zhang

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Enhanced securities investment strategy using ISSA–SVM: a hybrid model combining adaptive moving average, support vector machine, and multi-strategy sparrow search algorithm for im... by Wei Ni, Qingqing Chen, Xiaochen Guo, Yanan Liu

“…This study proposes a novel hybrid strategy, ISSA–SVM, that combines Adaptive Moving Average (AMA), Support Vector Machine (SVM), and an Improved Sparrow Search Algorithm (ISSA) to enhance CTA model performance in securities investment. …”
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Firefly algorithm with multiple learning ability based on gender difference by Wenning Zhang, Chongyang Jiao, Qinglei Zhou

“…Abstract The Firefly Algorithm (FA), while effective for complex optimization, suffers from inherent limitations such as search oscillation and low convergence precision. …”
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