A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards by Roni Azriel, Oded Degani, Avital Bechar

“…It compares the current approach for optimizing manipulator configurations, which relies on simulation and optimization algorithms, with an improved methodology that integrates machine learning models to enhance the optimization process. …”
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Improving quantum metrology protocols with programmable photonic circuits by de las Heras Alberto Muñoz, Porras Diego, González-Tudela Alejandro

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Rock image classification based on improved EfficientNet by Kai Bai, Zhaoshuo Zhang, Siyi Jin, Shengsheng Dai

“…Second, the shuffle attention mechanism is integrated into the backbone network to enhance feature extraction while reducing model parameters. Finally, the Lion optimizer is employed to optimize the training process, improving both the accuracy and stability of the model. …”
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Optimizing Power System Performance: The Significance of Placement and Sizing of Battery Energy Storage Systems by Harith B. Hussien, Ahmed J. Abid, Naseer M. Yasin, Ameer L. Saleh, Hayder Jasim Habil

“…To do this, two meta-heuristic optimization algorithms are suggested in this paper, Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). …”
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A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning by Junyao Ding, Jianchao Du, Hejie Wang, Song Xiao

“…Then, the improved genetic algorithm is used to search for the global optimal feature subset further. …”
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Optimizing P-Chart Performance Using a Percentage-Based Framework: Application to Nonconforming Items in the Manufacturing Sector by Zahid Khan, Aamir Saghir, Attila I. Katona, Zsolt T. Kosztyan

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Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. …”
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Structural Optimization-Based Enhancement of the Dynamic Performance for Horizontal Axis Wind Turbine Blade by Ahmed Zarzoor, Alaa Jaber, Ahmed Shandookh

“…This research demonstrates the transformative power of wind turbine blade optimization, it promotes innovation and sustainability in the field of clean energy by achieving 50% improvement in aerodynamic performance and 14% improvement in the modal frequency of the blade while maintaining the weight of the blade.…”
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Localization of Sensor Nodes in 3D Wireless Sensor Networks with a Single Anchor by an Improved Adaptive Artificial Bee Colony (iaABC) Algorithm by Dursun Ekmekci, Hüseyin Altınkaya

“…The method employs the Improved Adaptive Artificial Bee Colony (iaABC) algorithm, a model of the classical ABC algorithm. …”
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Energy-Optimized Path Planning and Tracking Control Method for AUV Based on SOC State Estimation by Guangyi Yang, Zhenning Xu, Feng Wang, Xiaoyu Zhang

“…This paper proposes a path planning and trajectory tracking control framework for autonomous underwater vehicles (AUVs) combined with battery state of charge (SOC) optimization. The framework incorporates the Grasshopper Optimization Algorithm (GOA) with the Artificial Potential Field Algorithm (APF) to achieve global path planning and local path optimization while minimizing energy consumption as an objective. …”
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Unmanned trackless rubber wheeler based on LD improved Cartographer mapping algorithm underground SLAM autonomous navigation method and test by Fangwei LI, Jiusheng BAO, Chen WANG, Xiaoming YUAN, Yan YIN, Shirong GE, Maosen WANG, Yuhan LV

“…Firstly, a two-dimensional raster map of the underground roadway environment is constructed based on the Cartographer algorithm, and the Lazy Decision (LD) algorithm is introduced to optimize it, which solves the overlapping and fuzzy phenomena during Cartographer mapping and improves the mapping accuracy. …”
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Dynamic Modeling and Parameter Optimization of Potato Harvester Under Multi-Source Excitation by Jianguo Meng, Zhipeng Li, Zheng Li, Yanzhou Li, Wenxia Xie

“…The optimization results demonstrate a 0.85 m/s² and 0.45 m/s² increase in RMS values for the soil-cutting disc and lifting chain, respectively, effectively enhancing the harvester’s work efficiency, while the frame exhibited a 0.31 m/s² reduction in RMS value, significantly improving structural stability. …”
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Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm by Mashuk Ahmed, Abdullah B. Nasser, Kamal Z. Zamli

“…In software testing, effective test case generation is essential as an alternative to exhaustive testing. For improving the software testing technology, the t-way testing technique combined with metaheuristic algorithm has been great to analyze a large number of combinations for getting optimal solutions. …”
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An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence by Stephen Oladipo, Yanxia Sun, Oluwatobi Adeleke

“…To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm’s convergence behaviour and aids both local and global search. …”
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Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province by Yang Li, Guoen Zhou, Jiaqi Xue, Junwei Yang, Shi Yin

“…To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). …”
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Integrated energy microgrids participating in voltage regulation ancillary services: An improved ADMM based distributed optimization approach by Hao Xiao, Wei Pei, Daoxin Han, Xiaowei Pu, Jiarui Wang

“…Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. …”
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An improved hybrid artificial bee colony algorithm for a multi-supplier closed-loop location inventory problem with customer returns. by Hao Guo, Xiaomei Lai, Ju Guo, Ge You, Ibrahim Alnafrah

“…The results of numerical experiments demonstrate that IHABC consistently outperforms competing methods, achieving up to 29.97% improvement in solution quality over the standard ABC algorithm. …”
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Research on the construction of English vocabulary learning recommendation system based on multi-objective crow search algorithm by Mengli Li

“…Regarding the experiments, we compared MOCSO with traditional single - objective optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). 56.4 % of users believe that the recommended vocabulary content meets their deep learning needs, while 18.2 % of learners hope that the system can further improve the practical application ability of vocabulary. …”
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Investigation of Floating Offshore Wind Farm Layout Optimization Considering Mooring Line Constraints by Haiying Sun, Mingdan Li, Tianhui Fan, Chenzhi Cai

“…By carefully considering the buffer zones, the power potential of FOWFs with three-line, four-line, and six-line mooring configurations can be improved by 122%, 100%, and 78%, respectively. Then, a genetic algorithm is employed to optimize the FOWT positions and mooring line angles simultaneously. …”
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An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and Particle Swarm Optimization by Borui Zhang, Bo Liu

“…Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. …”
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