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Showing 261 - 280 results of 7,867 for search '(( improved model optimization algorithm ) OR ( improve cost optimization algorithm ))', query time: 0.44s Refine Results
  1. 261

    An Improved Squirrel Search Algorithm for Optimization by Tongyi Zheng, Weili Luo

    Published 2019-01-01
    “…Squirrel search algorithm (SSA) is a new biological-inspired optimization algorithm, which has been proved to be more effective for solving unimodal, multimodal, and multidimensional optimization problems. …”
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    Article
  2. 262

    Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm by S. Bharath, A. Vasuki

    Published 2025-04-01
    “…Unlike traditional methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Crow Search Algorithm (CSA), which suffer from premature convergence and limited adaptability to real-time variations, Reinforcement Learning Enhanced Crow Search Algorithm (RL-CSA) which is proposed in this research work solves network reconfiguration optimization problem and minimize energy losses. …”
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    Article
  3. 263

    Real-time Detection Algorithm of Expanded Feed Image on the Water Surface Based on Improved YOLOv11 by ZHOU Xiushan, WEN Luting, JIE Baifei, ZHENG Haifeng, WU Qiqi, LI Kene, LIANG Junneng, LI Yijian, WEN Jiayan, JIANG Linyuan

    Published 2024-11-01
    “…To address these challenges and enhance the detection of floating extruded feed particles on the water surface, ,thereby providing precise decision support for intelligent feeding in intensive aquaculture modes, the YOLOv11-AP2S model, an advanced detection model was proposed.[Methods]The YOLOv11-AP2S model enhanced the YOLOv11 algorithm by incorporating a series of improvements to its backbone network, neck, and head components. …”
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    Article
  4. 264

    GIRH-Unet: Improved Residual Tobacco Segmentation Algorithm Based on GhostNetV3-Unet by Jianhua Ye, Yunda Zhang, Pan Li, Ze Guo

    Published 2025-01-01
    “…These factors contribute to the reduced accuracy and robustness of visual detection technologies based on segmentation algorithms within tobacco intelligent production systems, highlighting the need for a targeted segmentation model. …”
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  5. 265
  6. 266

    YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection by Xiang Gan, Shukun Cao, Jin Wang, Yu Wang, Xu Hou

    Published 2025-07-01
    “…The experimental results showed that the precision, recall, and mAP0.5 of the improved algorithm were improved by 1.4, 1.1, and 1.5%, respectively, compared with YOLOv8n, and the model parameters and computational costs were reduced by 6.6 and 7.3%, respectively. …”
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    Article
  7. 267

    Parameter Optimization of Milling Process for Surface Roughness Constraints by GUO Bin, YUE Caixu, ZHANG Anshan, JIANG Zhipeng, YUE Daxun, QIN Yiyuan

    Published 2023-02-01
    “… In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing.…”
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  8. 268

    CMACGSA: Improved Gravitational Search Algorithm Based on Cerebellar Model Articulation Controller for Optimization by Nazmiye Ebru Bulut, Emre Dandil, Ugur Yuzgec, Alpaslan Duysak

    Published 2025-01-01
    “…Recent advances in the field often involve hybrid methods that combine several algorithms to improve performance. This study introduces an improved Gravitational Search Algorithm, named CMACGSA, which incorporates the Cerebellar Model Articulation Controller (CMAC)-a neural network model-to enhance the performance of Gravitational Search Algorithm (GSA). …”
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    Article
  9. 269

    Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing by Lifei Wang, Yucheng Gu, Xiaoqing Tian, Jun Wang, Yan Jia, Junjie Xu, Zhen Zhang, Shiying Liu, Shuo Liu

    Published 2025-05-01
    “…Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. …”
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    Article
  10. 270

    Improved Lyrebird optimization for multi microgrid sectionalizing and cost efficient scheduling of distributed generation by Karthik Nagarajan, Arul Rajagopalan, Mohit Bajaj, Valliappan Raju, Vojtech Blazek, Lukas Prokop

    Published 2025-05-01
    “…This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. …”
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    Article
  11. 271

    Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model by JIANG Tao, XU Cong, JIA Shaohui, WANG Shen, ZHANG Yajian

    Published 2024-08-01
    “…Then, based on the differential evolution-particle swarm optimization algorithm, the established low-carbon planning model of the integrated energy system was solved to avoid the algorithm from falling into local optimality during the optimization process. …”
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  12. 272

    Capacity Optimization Method for Photovoltaic Hydrogen Production Systems Based on Multi-Objective Particle Swarm Algorithm by Haotian LU, Shaopeng LIU, Kai WANG

    Published 2025-05-01
    “…[Result] Taking the economic cost of the system, curtailment rate of solar power, and electricity purchase rate as optimization objectives, the multi-objective particle swarm algorithm was employed to solve the capacity configuration of the system components. …”
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  13. 273

    Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning by Carlo Ferro, Matteo Cafaro, Paolo Maggiore

    Published 2024-11-01
    “…Following trajectory optimization, the derived data are used to train an ML model that predicts setup parameters, significantly reducing computational costs and time. …”
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  14. 274

    Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements by Cheng Chen, Guan-Nian Chen, Song Feng, Xiao-Zhen Fan, Liang-Tong Zhan, Yun-Min Chen

    Published 2025-08-01
    “…This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. …”
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  15. 275

    Optimization of cable connection layout of unattended AC/DC joint construction station based on INFO algorithm by Long Hu, Zhiwei Yu, Minxin Liang, Xiaoying Chen, Lixia Chen, Guohui Huang

    Published 2025-05-01
    “…Using the INFO algorithm, the feasible solutions of the model are formed into a search domain and solved, and finally the optimal cable connection layout results are output. …”
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  16. 276

    Dynamic reconfiguration of the distribution systems with Load Duration Curve (LDC) model for reducing the losses and improving the voltage profile by Sana Sadeghi, Alireza Jahangiri, Ahmad Ghaderi Shamim

    Published 2024-05-01
    “…Simulations were conducted on the well-established IEEE 33-bus test system, employing MATLAB software in conjunction with a genetic algorithm to minimize losses and optimize voltage profiles. …”
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    Article
  17. 277

    An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection by Congxiao Jiang, Lingang Yang, Yuqing Gao, Jie Zhao, Wenne Hou, Fangmin Xu

    Published 2025-01-01
    “…We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. …”
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  18. 278

    Structural Simulation Model Updating Based on Improved MCMC Algorithm and Surrogate Model by MIAO Ji, DUAN Liping, LIU Jiming, LIN Siwei, ZHAO Jincheng

    Published 2025-08-01
    “…To enhance the accuracy of finite element model simulation, a model updating method based on Bayesian theory is proposed, and the updating efficiency is improved by integrating improved Markov chain Monte Carlo (MCMC) algorithm and surrogate model. …”
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  19. 279

    Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA by Bin Zhang, Jue Wang, Bo Li

    Published 2023-01-01
    “…In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. …”
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  20. 280

    Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment by Wentian Shang, Jinzhang Jia

    Published 2024-11-01
    “…However, current algorithms encounter challenges when applied to large-scale mines, primarily due to the complexity of variables and limited attention to optimizing main fans. …”
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    Article