AC Optimal Power Flow Problem Considering Wind Energy by an Improved Particle Swarm Optimization by Mohammad Reza Ansari, Hossein Ramzaninezhad

“…The proposed AC-OPF formulation includes the integer variables in addition to continuous variables and studies the effects of wind energy, transformer tap settings, and shunt capacitors on fuel cost, transmission losses as well as up and down spinning reserves. To solve the AC-OPF model, an Improved Particle Swarm Optimization (IPSO) is presented. …”
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YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection by Yuxi Huang, Hong Zhao, Jie Wang

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Integration of improved APF and RRT algorithms for enhanced path planning in mobile robotics by Yu Liangwu, Han Jianggui, Ma Boweng, Liu Bao, He Zhiying

“…Firstly, the mathematical model of the traditional APF algorithm was improved to solve the problems of unbalanced force and unreachable targets. …”
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Tracking Control of CSTRs Based on Improved OU Noise and the TD3 Algorithm by Hongyan Shi, Xiaofei Wu, Guogang Wang

“…To enhance exploration capabilities and convergence speed, fractional-order characteristics and a reward feedback mechanism are introduced into the OU noise, dynamically adjusting noise intensity to improve adaptability to complex states and optimize the exploration strategy of the TD3 algorithm. …”
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Dissolved Oxygen Prediction Based on SOA-SVM and SOA-BP Models by ZHANG Xuekun

“…To improve the accuracy of dissolved oxygen prediction,this paper researches and proposes a prediction method that combines seagull optimization algorithm (SOA) with support vector machine (SVM) and BP neural network,prepares four prediction schemes based on the monthly dissolved oxygen monitoring data of the Jinghong Power Station in Xishuangbanna,a national important water supply source in Yunnan Province,from January 2009 to September 2020,optimizes the key parameters of SVM and weight threshold of BP neural network by SOA to construct SOA-SVM and SOA-BP models,predicts the dissolved oxygen of Jinghong Power Station based on the models,and compares the prediction results with those of SVM and BP models.The results show that:The absolute values of the average relative errors of the SOA-SVM and SOA-BP models for the 4 schemes of dissolved oxygen prediction are between 4.07%~4.98% and 3.85%~4.83%,and that of the average absolute errors are 0.309~0.374 mg/L and 0.294~0.371 mg/L,respectively.With better prediction accuracy than SVM and BP models,they have good prediction accuracy and generalization ability.SOA can effectively optimize the key parameters of SVM and weight threshold of BP neural network.SOA-SVM and SOA-BP models are feasible for dissolved oxygen prediction,which can provide references for related prediction research.…”
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Small object detection algorithm based on improved YOLOv10 for traffic sign by Yukang Zou, Scarlett Liu

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A Lightweight Detection Method for Meretrix Based on an Improved YOLOv8 Algorithm by Zhongxu Tian, Sifan Hou, Xiaoxue Yue, Xuewen Hu

“…Clam farms are typically located in remote areas with limited computational resources, making it challenging to deploy traditional deep learning-based object detection methods due to their large model size and high computational demands. To address this issue, this paper proposes a lightweight detection method, YOLOv8-RFD, based on an improved YOLOv8 algorithm, tailored for clam sorting applications. …”
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MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke by Ting Zhou, Dandan Li, Jingfang Zuo, Aihua Gu, Li Zhao

“…Results The proposed MSKT-NSGP model outperformed traditional algorithms in prediction accuracy, computational efficiency, and uncertainty handling. …”
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Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario by Sifeng ZHU, Yu WANG, Hao CHEN, Hai ZHU, Zhengyi CHAI, Chengrui YANG

“…In the context of the Internet of vehicles, the placement and deployment number of edge servers directly affect the efficiency of edge computing.Due to the high cost of deploying a large edge server on a macro base station and a base station, it can be complemented by deploying a small edge server on a micro base station, and the cost reduction needs to be optimized by optimizing the placement of large edge servers.In order to minimize the deployment cost and service delay of the edge server, and maximize the operator’s revenue and server load balance, the edge server placement problem combined with the vehicle networking user application service was modeled as a multi-objective optimization problem and a placement scheme based on improved NSGA-Ⅱ algorithm was proposed.The experimental results show that the proposed scheme can reduce the deployment cost of edge servers by about 44%, the latency by about 14.2%, and improve the revenue of operators by 24.2%, which has good application value.…”
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An Improved YOLOv7-Tiny-Based Algorithm for Wafer Surface Defect Detection by Mengyun Li, Xueying Wang, Hongtao Zhang, Xiaofeng Hu

“…Experiments conducted on a self-constructed dataset show that the improved algorithm achieved a mean Average Precision (mAP) of 90.1%, representing a 3.2% increase over the original algorithm. …”
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Improved salp swarm algorithm-driven deep CNN for brain tumor analysis by Umang Kumar Agrawal, Nibedan Panda, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

“…This leads to improved model performance, characterized by higher accuracy, reduced standard deviation, lower minimum RMSE values, and higher average performance. …”
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Small Ship Detection Based on Improved Neural Network Algorithm and SAR Images by Jiaqi Li, Hongyuan Huo, Li Guo, De Zhang, Wei Feng, Yi Lian, Long He

“…Therefore, based on the YOLOv5s model, this paper improves its backbone network and feature fusion network algorithm to improve the accuracy of ship detection target recognition. …”
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Optimization of Gantry Cranes’ Operation Path for Transshipment Based on Improved TSP by Qi Zhang, Hongjin Dong, Mingjun Ling, Leyi Duan, Yuguang Wei

“…Based on the basic model of TSP, the paper constructed the optimization model for the operation path of RMG, and designed the Ant Colony Algorithm (ACA) to solve it, and then obtained the operation scheme of RMG having the highest efficiency. …”
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Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm by Xuyan Tan, Weizhong Chen, Luyu Wang, Wei Ye

“…Therefore, with the aim of optimizing the monitoring scheme, this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm. …”
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The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction by Hongping Hu, Yangyang Li, Yanping Bai, Juping Zhang, Maoxing Liu

“…The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. …”
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Developing a Machine Learning-Driven Model that Leverages Meta-Heuristic Algorithms to Forecast the Load-Bearing Capacity of Piles by Tianhua Zhou

“…Findings show that the GPR-GJO model provides the most accurate Pu predictions, emphasizing its potential to optimize pile design, mitigate risks, and ensure long-term structural safety.…”
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Research on collaborative filtering algorithm based on improved K-means algorithm for user attribute rating and co-rating by ShengShai Zhang, Shiping Chen, Xiaodong Yu, Shaowei Mei

“…Finally, analyses conducted using two distinct datasets indicated that our enhanced KUR-CF model achieved improvements in Precision values by 60% and Recall values by 35%, relative to other conventional collaborative filtering algorithms. …”
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Donor Segmentation Analysis Using the RFM Model and K-Means Clustering to Optimize Fundraising Strategies by Rezki ., Nouval Trezandy Lapatta, Rizka Ardiansyah, Wirdayanti ., Dwi Shinta Angreni

“…This study aims to segment donors using the Recency, Frequency, Monetary (RFM) model and the K-Means algorithm to optimize fundraising strategies. …”
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An Improved DGA Feature Clustering-Based Method for Transformer Fault Diagnosis by Yujie Zhang, Jian Feng, Shanyuan Wang

“…At present, intelligent fault diagnosis methods for power transformers are mostly based on classification algorithms, but the diagnosis models may be relatively complicated. …”
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Intelligent analysis algorithm for power engineering data based on improved BiLSTM by Yuanyuan Xu, Jiapeng Yang, Xin Cai

“…In equipment fault diagnosis, the accuracy of improved BiLSTM under current, voltage, temperature and pressure is significantly higher than that of models such as GRU.…”
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