Multi-Objective Parameter Optimization of Electro-Hydraulic Energy-Regenerative Suspension Systems for Urban Buses by Zhilin Jin, Xinyu Li, Shilong Cao

“…To streamline multi-objective optimization processes, a particle swarm optimization–back propagation (PSO-BP) neural network surrogate model was developed to approximate the complex co-simulation system. …”
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Numerical Simulation and Optimization of Wind Effects of Porous Parapets on Low-Rise Buildings with Flat Roofs by Ye Qiu, Bingbing San, Youyi Zhao

“…This paper presents a procedure to optimize the porosity of parapets to improve the aerodynamic behavior of low-rise buildings with flat roofs, by coupling an optimization algorithm and computational fluid dynamics (CFD) simulations. …”
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An Optimal Charging Strategy for PV-Based Battery Swapping Stations in a DC Distribution System by Shengjun Wu, Qingshan Xu, Qun Li, Xiaodong Yuan, Bing Chen

“…Then, a particle swarm optimization (PSO) algorithm is developed to calculate the optimal charging power and to minimize the charging cost for each time slot. …”
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Study on the peak shaving operation of cascade hydropower stations based on the plant-wide optimal curve by Fengshuo Liu, Kui Huang, Xuanyu Shi, Longqing Zhao, Yangxin Yu, Xueshan Ai, Xiang Fu

“…This study proposes a novel method to enhance RES and hydropower utilization through: 1) Establishing a plant-wide optimal output-water level-outflow relationship curve based on the output-head-flow relationship and the flow-head loss and outflow-tailwater level relationship curves for each unit; 2) Developing a short-term peak shaving model for cascaded hydropower stations that incorporates wind and PV power which defines minimum coefficient of variation of residual load as objective functions, and improving discrete differential dynamic programming successive approximation (DDDPSA) method to solve it; 3) Analyzing the peak shaving capacity of the cascade hydropower station by varying water consumption for power generation. …”
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Optimization of Urban Rail Transit Train Stock Utilization under Re-coupling Operation Mode by DENG Chengyuan, LU Jinsheng, ZHU Changfeng, SUN Yuanguang, MA Bin

“…The MTSP ( multiple traveling salesman problem) is introduced to construct an optimized train stock utilization model during the transition period from peak to off-peak passenger flow in urban rail transit. …”
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Synergistic hyperspectral and SAR imagery retrieval of mangrove leaf area index using adaptive ensemble learning and deep learning algorithms by Jun Sun, Weiguo Jiang, Bolin Fu, Hang Yao, Huajian Li

“…We confirmed that 1D-CNN + DNNR provided an effective approach to estimating the mangrove LAI, as it produced a higher-accuracy inversion (R2 = 0.8685) than that of the AELR model. It was found in this study that the 1D-CNN improved the retrieval accuracy (R2) of the mangrove LAI from 0.097 to 0.1297 when compared with the traditional data dimension reduction (DDR) method, which demonstrated that the 1D-CNN was able to improve the inversion accuracy of the mangrove LAI. …”
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Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton by Yi Long, Zhi-jiang Du, Wei-dong Wang, Wei Dong

“…A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. …”
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Optimized dual NURBS curve interpolation for high-accuracy five-axis CNC path planning by Yi Xu, Mohd Hairi Mohd Zaman, Feng Zhou

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Adaptive RFID Data Scheduling Using Proximal Policy Optimization for Reducing Data Processing Latency by Guowei Guo, Xinsen Yang, Ziwei Liang, Zeli Xi, Ximei Zhan, Peisong Li

“…This paper presents a novel approach for dynamically offloading data using deep reinforcement learning, specifically employing the Proximal Policy Optimization (PPO) algorithm. The proposed method utilizes a central controller equipped with the PPO model to make intelligent, real-time reader selection decisions based on environmental factors such as reader load, tag mobility, and network conditions. …”
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Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments by Zichong Wang, Yingying Zheng

“…Finally, a public dataset was utilized for the experiments to verify the performance of the proposed algorithm. Comparisons and discussions show that the proposed optimization strategies significantly improve the performance of PMOEVO, demonstrating marked advantages over six classical algorithms. …”
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Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation by Azzah Allahim, Asma Cherif

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Electromagnetic Analysis and Multi-Objective Design Optimization of a WFSM with Hybrid GOES-NOES Core by Kyeong-Tae Yu, Hwi-Rang Ban, Seong-Won Kim, Jun-Beom Park, Jang-Young Choi, Kyung-Hun Shin

“…The optimized WFSM achieved a 13.97% increase in power density and a 1.0% improvement in efficiency compared to the baseline NOES model. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. We hypothesize that incorporating soil moisture and temperature data from land surface models into a ML framework will enhance accuracy of corn yield predictions. …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

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Optimal Configuration of Hybrid Energy Storage Capacity for Wind Farms Considering Carbon Trading Revenue by Chongde CHEN, Qiang GUO, Ziqiu SONG, Yang HU

“…In view of the cost of initial investment and replacement of energy storage as well as the revenue from electricity sales and carbon trading, an optimal configuration model of hybrid energy storage capacity for wind farms was established and solved by an adaptive chaotic particle swarm optimization algorithm. …”
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Multi-Agent Deep Reinforcement Learning for Scheduling of Energy Storage System in Microgrids by Sang-Woo Jung, Yoon-Young An, BeomKyu Suh, YongBeom Park, Jian Kim, Ki-Il Kim

“…To defeat the above issues, in this paper, we propose a new DRL-based scheduling algorithm using a multi-agent proximal policy optimization (MAPPO) framework that is combined with Pareto optimization. …”
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Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network by Vijaya Gunturu, J. Kavitha, Swapna Thouti, N. K. Senthil Kumar, Kamal Poon, Ayman A. Alharbi, Amar Y. Jaffar, V. Saravanan

“…The Emperor Penguin Optimizer Algorithm (EPOA) was used to select the features sent from the Arduino board to the ESP8266-Wi-Fi module. …”
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Distributed robust planning for new power system considering uncertainty and frequency security by Xinxin Zheng, Dahai Zhang, Zhaohong Bie, Xue Wu

“…Firstly, establish a system frequency model that provides frequency support for a fast frequency response energy storage system, and propose an improved segmented linearization algorithm to transform the constructed frequency safety constraints. …”
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Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control by Foad Hamzeh, Siavash Fathollahi Dehkordi, Alireza Naeimifard, Afshin Abyaz

“…A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. …”
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Cooperative and Hierarchical Optimization Design of Shipboard MVDC System for Adapting to Large, Pulsed Power Load by Zhimeng Liu, Yongbao Liu, Youhong Yu, Rui Yang

“…The hierarchical optimization model is designed with energy storage configuration and dynamic performance as the lower and upper objectives, and an efficient parallel neural network-based genetic algorithm is employed to solve this optimization. …”
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