Real-Time Optimization of Discrete Element Models for Studying Asphalt Mixture Compaction Characteristics at the Meso-Scale by Xue Wang, Zifang Wang, Xuanye Luo

“…However, improving simulation accuracy while maintaining computational efficiency remains a challenge. …”
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DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks by Alireza Barekatain, Hamed Habibi, Holger Voos

“…Furthermore, we propose an optimization-based smoothing algorithm that ensures time-optimal and jerk-regulated demonstration trajectories, while also adhering to the robot’s kinematic constraints. …”
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Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks. by Jihong Wang, Hao Ni, Yiyang Ge, Shuo Li

“…To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. …”
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CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network by Hamed Safikhani, Somayeh Davoodabadi Farahani, Lakhbir Singh Brar, Faroogh Esmaeili

“…The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). …”
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A stochastic optimization method for economic microgrid operation based on "source-load operation" mode classification by ZHOU Hang, MA Lihong, LI Zhongzhong, WANG Haisheng, LI Sifan, ZHANG Changjun, FU Linbei

“…Finally， the adaptive weighted particle swarm optimization （AW-PSO） is employed to solve the stochastic optimization model for microgrid operations. …”
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A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet by Kaizhe Chen, Jin Liu, Wenjing Lyu, Tianyuan Wang, Jinxi Wen

“…While extensive research has been conducted on AI’s role in transportation innovation, there remains a significant gap in empirical studies focusing on optimizing the charging behavior of operational EV fleets, particularly within ride-hailing services. …”
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A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing by Walid Abdallah Shobaki, Mariofanna Milanova

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Structural optimization of stiffened panel structures with continuous and discrete design variables using deep reinforcement learning by Ryota NONAMI, Mitsuru KITAMURA

“…Optimizing stiffened panel structures used in ships and aircrafts are becoming increasingly important for reducing material costs while maintaining or enhancing structural strength. …”
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Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration by Guang Yang, Jun Wang, Zhengyuan Qi

“…The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. …”
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Efficient Hyperparameter Optimization Using Metaheuristics for Machine Learning in Truss Steel Structure Cross-Section Prediction by Donwoo Lee, Seunghyeon Noh, Jeonghyun Kim, Seungjae Lee

“…The optimal design of truss structures is one of the most complex problems, as it requires achieving high stiffness and stability while pursuing lightweight structures. …”
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Multidimensional routing with the increased navigation accuracy with the maintenance of flight notifications of the flight vehicles by V. I. Goncharenko, G. N. Lebedev, V. B. Malygin

“…Optimal solution can be obtained both by the enumeration of the solution versions and with the help of the genetic algorithm, which is allowed for a smaller number of iterations, to obtain suboptimal in real time, which corresponds to the conditions of the task solution. …”
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Gray Wolf Optimizer With Communication Strategy Based on DV-Hop for Nodes Location of Wireless Sensor Networks by Dan Yu, Ting Yuan, Haiyin Qing, Wenwu Xie, Peng Zhu

“…Comprehensive experimental analyses on established benchmark functions have validated the efficacy of the IGWO-CS method, illustrating its competitiveness with other advanced algorithms such as the Ranking-based Adaptive Cuckoo Search (RACS) and Modified Particle Swarm Optimization (MPSO). …”
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NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions by Youliang Chen, Wencan Guan, Rafig Azzam, Siyu Chen

“…This study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. …”
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Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor Networks by Nelofar Aslam, Hongyu Wang, Muhammad Farhan Aslam, Muhammad Aamir, Muhammad Usman Hadi

“…Our findings indicate that the outage-optimal distance achieved by WPCD-ACO is 6092 m, compared to 7225 m for the shortest path and 6142 m for Dijkstra’s algorithm. …”
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Stackelberg Game Optimization Strategy of Virtual Power Plants and Electric Vehicles Based on Conditional Value-at-Risk by MA Qianxin, JIA Heping, GUO Yuchen, LI Peijun, YANG Ye, LIU Dunnan, ZHAO Zhenyu

“…The lower-level model minimized user costs by responding to these prices using a utility function that captures both cost satisfaction and charging experience. A particle swarm optimization algorithm was employed to solve the coupled model and identify the equilibrium strategies.…”
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Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques by Mohamed Bechir Ben Hamida, Ali B. M. Ali, Narinderjit Singh Sawaran Singh, Loghman Mostafa

“…Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. …”
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Evaluating end-to-end autonomous driving architectures: a proximal policy optimization approach in simulated environments by Ângelo Morgado, Kaoru Ota, Mianxiong Dong, Nuno Pombo

“…The study uses the Proximal Policy Optimization (PPO) algorithm within the CARLA simulation environment. …”
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Optimal Economic Dispatch Strategy for Cascade Hydropower Stations Considering Electric Energy and Peak Regulation Markets by Fan Liu, Wentao Huang, Jingjing Ma, Jun He, Can Lv, Yukun Yang

“…Specifically, firstly, the strategy adopts multi-objective optimization. The objective function takes into account the generation capacity of the cascade hydropower stations, the benefits of the EEM, the influence of the spot market, the compensatory benefits of peaking, and the sharing expenses of peaking; secondly, the constraints at the level of the power grid, the level of the cascade hydropower stations, and the level of the market are taken into account comprehensively, and the Harris Hawk Algorithm is used to solve the model; lastly, by comparing different schemes, it is observed that under varying inflow conditions, the proposed dispatch strategy in this paper yields slightly lower revenue in the EEM than other schemes. …”
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Optimal scheduling of district heat pumps conceived for implementation in Energy Management Systems to participate in demand response by Roberto Rocca, Stefano Leonori, Gregorio Fernández Aznar, Riccardo Toffanin, Luis Luengo-Baranguan

“…To this end, this work proposes a novel formulation of the optimal scheduling problem of district-level HPs, conceived for EMS implementation, and based on a quadratic programming algorithm, with a specific objective function for IDR and DDR. …”
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Optimization and analysis of interior permanent magnet drive motor with unequal thickness magnetic poles for electric vehicle by Shilun MA, Yongyi WU, Changwei LI, Keqi CHEN

“…Finally, with torque ripple, cogging torque, and output torque as optimization objectives, the particle swarm optimization algorithm was used to perform multi-objective optimization on the maximum magnetic pole thickness, minimum magnetic pole thickness, the width of magnetic pole, the depth and width of the rotor auxiliary slots, in order to determine the optimal parameters, and a prototype was then developed for experimental testing. …”
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