Optimization of the Offshore Wind Turbines Layout Using Cuckoo Search Algorithm by Ibrahim Abdulwahab, Solomon Andrew Onazi, Gideon Rimamfate, Akande Abdulwasiu, Abubakar Umar, N.A Iliyasu

“…After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). …”
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Optimized conductor selection and phase balancing in unbalanced distribution networks: Economic optimization via the vortex search algorithm by Brandon Cortés-Caicedo, Jhony Andrés Guzmán-Henao, Oscar Danilo Montoya, Luis Fernando Grisales-Noreña, Rubén Iván Bolaños

“…This methodology is compared against the hurricane optimization algorithm, the sine cosine algorithm, and the salp swarm optimization algorithm. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…To reduce the incidence of apple diseases and increase fruit yield, developing efficient and fast apple leaf disease detection technology is of great significance. An improved YOLOv8 algorithm was proposed to identify the leaf diseases that occurred during the growth of apples.…”
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Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images by Kaiyuan Long, Shibo Li, Jiangping Long, Hui Lin, Yang Yin

“…Lastly, our proposed Siblings Detection Head reduces computational burden while significantly improving detection performance. …”
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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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A Lightweight Detection Method for Meretrix Based on an Improved YOLOv8 Algorithm by Zhongxu Tian, Sifan Hou, Xiaoxue Yue, Xuewen Hu

“…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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Application of improved and efficient image repair algorithm in rock damage experimental research by Mingzhe Xu, Xianyin Qi, Diandong Geng

“…The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis.…”
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Application of MRI image segmentation algorithm for brain tumors based on improved YOLO by Tao Yang, Xueqi Lu, Lanlan Yang, Miyang Yang, Jinghui Chen, Hongjia Zhao

“…ObjectiveTo assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.MethodsThe research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. …”
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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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MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design by Anoop Raghuvanshi, Abhinav Sharma, Abhishek Kumar Awasthi, Abhishek Sharma, Rahul Singhal, Kim Soon Chong, Sew Sun Tiang, Wei Hong Lim

“…This paper presents the modified arithmetic optimization algorithm (MAOA), a swift and effective optimization algorithm specifically designed for electromagnetic applications. …”
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Building Energy Optimization Using an Improved Exponential Distribution Optimizer Based on Golden Sine Strategy Minimizing Energy Consumption Under Uncertainty by Mohammad Ali Karbasforoushha, Mohammad Khajehzadeh, Suraparb Keawsawasvong, Lapyote Prasittisopin, Thira Jearsiripongkul

“…In this study, a new improved meta-heuristic algorithm is proposed for solving the energy building optimization (EBO) and also hybrid energy systems optimization considering uncertainty of conditioned surface area subjected to temperature control for BEO and renewable power and load uncertainties for hybrid system. …”
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A hybrid renewable energy system for Hassi Messaoud region of Algeria: Modeling and optimal sizing by Yacine Bourek, El Mouatez Billah Messini, Chouaib Ammari, Mohamed Guenoune, Boulerbah Chabira, Bipul Krishna Saha

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Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method by Er Wang, Tianbao Huang, Zhi Liu, Lei Bao, Binbing Guo, Zhibo Yu, Zihang Feng, Hongbin Luo, Guanglong Ou

“…The Lasso-GA method results in an ETR model with an R² of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R² of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R² by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R² by 0.04 and reduces RMSE by 1.93 Mg/ha. …”
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Better care for children with appendicitis: implementation of antibiotic stewardship optimizes postoperative therapy by Beltz, Sebastian, Fischer, Stephanie, Huenger, Frank, Vahdad, Reza, Kalhoff, Hermann, Leutner, Andreas

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Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning by Elmoundher Hadjaidji, Mohamed Cherif Amara Korba, Khaled Khelil

“…This study aims to refine PD detection systems by introducing customized weighting to acoustic features, adjusting their significance based on their correlation with the disease and the classification algorithm utilized. The particle swarm optimization algorithm is employed for this purpose, with the Oxford PD dataset serving as the source data for training and validation. …”
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Bayesian optimization with Optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization by Bamidele A. Dada, Nnamdi I. Nwulu, Seun O. Olukanmi

“…In addition, it examines 2,000 random surface soil samples, ranging from 0 to 20 cm, that were optimized using genetic algorithms (GA), particle swarm optimization (PSO), and Optuna. …”
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Energy optimization in intelligent sensor networks: application of particle swarm optimization algorithm in the deployment of electronic information sensing nodes by Wang Liang

“…The focus is on maximizing the monitored area while minimizing energy usage. A Scalable coverage-based particle swarm optimization (SCPSO) algorithm integrates a probabilistic coverage model based on Euclidean distance to detect coverage gaps and guide the optimal positioning of nodes, ensuring that each target within the region of interest is covered by at least one sensor. …”
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An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm by Wei Chen, Qike Cao, Bingyu Cao, Bo Jin

“…Comparative experiments with Improved Artificial Bee Colony Algorithm (IABC), Chaotic Adaptive Firefly Optimization Algorithm (CAFA), Adaptive Particle Swarm Optimization (APSO), and Lévy Flight Strategy Chaotic Snake Optimization Algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. …”
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An Optimization Method for Multi-Robot Automatic Welding Control Based on Particle Swarm Genetic Algorithm by Lu Chen, Jie Tan, Tianci Wu, Zengxin Tan, Guobo Yuan, Yuhao Yang, Chiang Liu, Haoyu Zhou, Weisi Xie, Yue Xiu, Gun Li

“…The results demonstrated that the optimized production line significantly improved efficiency while maintaining safety standards.…”
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Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization by Xue Li, Yixuan Yang, Zheng Li, Hui He

“…Specifically, it sets a population size of 50, a maximum number of iterations of 200, a migration rate dynamically adjusted between 0.6-0.9, and an adaptive mutation rate of 0.01-0.05, using root mean square error and response time as performance evaluation metrics for parameter optimization. Experimental data show that compared to traditional methods, this strategy can increase the average operating speed of trains by 15%, reduce signal delays by 20%, and improve system robustness indicators by 18.5%, achieving a collaborative enhancement of efficiency and safety while ensuring stable operation, thus providing an engineering-valued solution for the intelligent upgrade of railway transport.…”
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