Urban Microcirculation Traffic Network Planning Method Based on Fast Search Random Tree Algorithm by Ning YU

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Automated guided vehicle (AGV) path optimization method based on improved rapidly-exploring random trees by Zhigang Ren, Anjiang Cai, Feilong Xu

“…In response to the issues of low computational efficiency, slow convergence speed, curvy paths, and the tendency to fall into local optima in rapidly-exploring random tree (RRT) algorithms for automated guided vehicle (AGV) path planning, this article proposes an improved RRT algorithm that combines adaptive step-size optimization with K-dimensional tree (KD-Tree) based fast nearest neighbor search. …”
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A Linear Fitting Algorithm Based on Modified Random Sample Consensus by Yujin Min, Yun Tang, Hao Chen, Faquan Zhang

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TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. by Xiaozhi Du, Kai Chen, Hongyuan Du, Zongbin Qiao

“…In the first stage of TS-SSA, this paper proposes a many-objective sparrow search algorithm (MaOSSA) to mainly manages the convergence through the adaptive population dividing strategy and the random bootstrap search strategy. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The objective is to identify predictors of dropout and enhance intervention strategies.MethodsThe study utilized seven base machine learning algorithms to create a stacked ensemble model with three meta-learners: Random Forest (RF), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost). …”
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NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm by Zhu Yaolin, Li Zheng, Chen Xin, Chen Jinni, Zhang Hongsong

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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. …”
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Enhanced Automatic Generation Control in Multiarea Power Systems: Crow Search Optimized Cascade FOPI‐TIDDN Controller With Integrated Renewable Solar Thermal Models and HVDC Lines... by Naladi Ram Babu, Pamarthi Sunitha, Ganesh Pardhu B. S. S., Sanjeev Kumar Bhagat, Adireddy Ramesh, Arindita Saha, Wulfran Fendzi Mbasso, Pradeep Jangir, Ahmed Hossam‐Eldin

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Research on vehicle scheduling for forest fires in the northern Greater Khingan Mountains by Jie Zhang, Junnan He, Shihao Ren, Pei Zhou, Jun Guo, Mingyue Song

“…Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm’s solution efficiency and accuracy, through the northern pristine forest area of Daxing’anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. …”
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Smart Chaining: Templing and Temple Search by Rajeev Ranjan Kumar Tripathi, Rahul Mishra, Shailesh Kumar Agrahari, Pradeep Kumar Singh, Sarvpal Singh

“…Experimental evidence illustrates the dominance of Templing and Temple Search as it delivers <inline-formula> <tex-math notation="LaTeX">$4.5x$ </tex-math></inline-formula> faster searches and <inline-formula> <tex-math notation="LaTeX">$3.5x$ </tex-math></inline-formula> faster insertions over plain chaining at equal space complexity. …”
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Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty by Chao Chen, Weifeng Peng, Cheng Xie, Shufeng Dong, Yibo Hua

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Streamlining ICI Transformed as a Nonnegative System by David Hyland

“…The previous algorithm used initial conditions that were randomly assorted pixel intensities. …”
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An RFCSO-based grid stability enhancement by integrating solar photovoltaic systems with multilevel unified power flow controllers by Swetha Monica Indukuri, Alok Kumar Singh, D. Vijaya Kumar

“…Furthermore, the ML-UPFC, controlled by a random forest cuckoo search optimization algorithm, enhances the fault ride-through capabilities and power regulation. …”
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Optimization Methods for Customized Bus Routes in Random Environments by Fangyuan Gong, Chuanjun Jia, Xu Wu

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Multiple objectives escaping bird search optimization and its application in stock market prediction based on transformer model by Dedai Wei, Zimo Wang, Minyu Qiu, Juntao Yu, Jiaquan Yu, Yurun Jin, Xinye Sha, Kaichen Ouyang

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Illustration Design Model with Clustering Optimization Genetic Algorithm by Jing Liu, Qixing Chen, Xiaoying Tian

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Comparative analysis of machine learning algorithms for predicting depression among individuals with diabetes by Hind Bourkhime, Noura Qarmiche, Soumaya Benmaamar, Nada Lazar, Mohammed Omari, Mohamed Berraho, Nabil Tachfouti, Samira El Fakir, Hanan El Ouahabi, Nada Otmani

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Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* by Xiqing Zhang, Pengyu Wang, Yongrui Guo, Qianqian Han, Kuoran Zhang

“…Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. …”
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Precise wind allocation scheme decision based on attraction-repulsion algorithm by NI Jingfeng, CHEN Dunwei, LIU Yujiao

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Routing algorithm for heterogeneous computing force requests based on computing first network by ZHANG Gang, LI Xi

“…An optimized genetic algorithm to address this issue was proposed. This algorithm was designed from both local and global perspectives: to ensure fast convergence to the target solution locally, a single parameter satisfying the randomness strategy was used to initialize the population, making it widely dispersed in the solution space; adopting a multi-parameter solution (or path) balanced selection strategy for selection operations, making the selected population rich and diverse; adopting a two-layer crossover strategy for crossover operations, with the aim of expanding the breadth of global search; adopting a multi parameter random single point mutation strategy for mutation operations, with the aim of deepening local search capabilities. …”
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