Gradual Optimization of University Course Scheduling Problem Using Genetic Algorithm and Dynamic Programming by Xu Han, Dian Wang

“…To improve the computational efficiency and solution quality, a hybrid method combining a genetic algorithm and dynamic programming, named POGA-DP, was designed. …”
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Fleet management algorithm for enhancing environmental friendliness of maritime delivery by Андрій Романов

“…The study proposes an improved algorithm for constructing maritime cargo delivery routes using a genetic algorithm with a speed optimization step. …”
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Stability optimization of dynamic migration algorithm for Post-Copy of virtual machine based on KVM by Shuangxi CHEN, Ruoyan ZHAO, Hui LIU, Chunming WU, Wei RUAN

“…The dynamic migration technology of virtual machine provides a strong support for the resource scheduling of virtualization system.As one of the two core algorithms of virtual machine dynamic migration, Post-Copy algorithm has been a hot issue for scholars at home and abroad for its advantages of stable migration time and short migration downtime.The fault tolerance mechanism of virtual machine, the connection between the transfer mode of memory page and the page missing error during the migration process, and the source code of QEMU-KVM platform were studied.A fault tolerant method based on transaction synchronization was proposed to improve the stability of the Post-Copy migration algorithm.The experimental results show that the proposed algorithm can ensure the fast repair of deep end virtual machine failure, target virtual machine failure and network fault during the migration process, and solve the stability problem at a small cost.The method proposed effectively improves the stability of the Post-Copy migration algorithm, and provides reference for the future optimization research direction.…”
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Economic operation analysis of the power grid combining communication network and distributed optimization algorithm by Yi Zhifei

“…In the verification of the economic dispatch problem, the total output power of the distributed optimization algorithm reached 13594.87 MW, and the economic cost was reduced by about 23%. …”
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Optimal Location through Distributed Algorithm to Avoid Energy Hole in Mobile Sink WSNs by Li Qing-hua, Gui Wei-hua, Chen Zhi-gang

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Two-Stage Global–Local Aerodynamic/Stealth Optimization Method Based on Space Decomposition by Wei Zhang, Lin Zhou, Bowen Shu, Xian Chen, Zhenghong Gao, Jiangtao Huang

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Joint Optimization Algorithm for UAV-Assisted Caching and Charging Based on Wireless Energy Harvesting by Yumeng Zhu, Qi Zhu

“…The joint optimization problem is divided into three subproblems, which use the Lagrange multiplier method, a simulated annealing algorithm, and a particle swarm optimization algorithm. …”
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Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm. by Ali Bahrami, Morteza Rakhshaninejad, Rouzbeh Ghousi, Alireza Atashi

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Energy Efficiency and Reliability in Underwater Wireless Sensor Networks Using Cuckoo Optimizer Algorithm by M. Dosaranian-Moghadam, Z. Amo-Rahimi

“…In the proposed algorithm, by presenting a cost function in COA algorithm, a hop-by-hop method of route selection is performed using power consumption and energy content of the current node; while in Greedy Geographic Forwarding based on Geospatial Division (GGFGD) algorithm, data transfer is based on the closest route to a destination criterion. …”
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A FPGA Accelerator of Distributed A3C Algorithm with Optimal Resource Deployment by Fen Ge, Guohui Zhang, Ziyu Li, Fang Zhou

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Improving Earth surface temperature forecasting through the optimization of deep learning hyper-parameters using Barnacles Mating Optimizer by Zuriani Mustaffa, Mohd Herwan Sulaiman, Muhammad ‘Arif Mohamad

“…To improve the DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) is integrated to optimize both weights and biases. …”
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A Hybrid Multi-Strategy Differential Creative Search Optimization Algorithm and Its Applications by Yuanyuan Zhang, Longquan Yong, Yijia Chen, Jintao Yang, Mengnan Zhang

“…The comparison includes both recent state-of-the-art algorithms and improved optimization methods. Simulation results demonstrate that the incorporation of the refined set and clustering process, along with the table reinforcement learning model (double Q-learning model) mechanism, leads to superior convergence speed and higher optimization precision.…”
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A Lightweight Pavement Defect Detection Algorithm Integrating Perception Enhancement and Feature Optimization by Xiang Zhang, Xiaopeng Wang, Zhuorang Yang

“…To address the current issue of large computations and the difficulty in balancing model complexity and detection accuracy in pavement defect detection models, a lightweight pavement defect detection algorithm, PGS-YOLO, is proposed based on YOLOv8, which integrates perception enhancement and feature optimization. …”
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Research on Investment Estimation of Prefabricated Buildings Based on Genetic Algorithm Optimization Neural Network by Jin Gao, Wanhua Zhao

“…Starting from the investment decision-making stage of construction projects, this paper analyses the characteristics of prefabricated investment estimation and the relevant literature on the characteristics of prefabricated construction projects, uses the rough set attribute reduction algorithm to screen the key engineering characteristic factors, and establishes a BP neural network model optimized by genetic algorithm to estimate and analyze the investment of completed prefabricated construction projects. …”
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Configurational Comparison of a Binary Logic Transmission Unit Applicable to Agricultural Tractor Hydro-Mechanical Continuously Variable Transmissions and Its Wet Clutch Optimizati... by Wenjie Li, Zhun Cheng, Mengchen Yang

“…The WOA improved the spread value in the GRNN algorithm, establishing a GRNN to predict the optimal range for wet clutch design values in BLT-U; the model validation showed an average correlation coefficient of 0.92 for speed curves and an average relative error of 5.58% for dynamic loads. …”
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Fluid–Structure Interaction Study in Unconventional Energy Horizontal Wells Driven by Recursive Algorithm and MPS Method by Xikun Gao, Dajun Zhao, Yi Zhang, Yong Chen, Zhanzhao Gao, Xiaojiao Zhang, Shengda Wang

“…This study presents a novel bidirectional fluid–structure interaction (FSI) model, uniquely integrating recursive algorithms with the Moving Particle Semi-implicit (MPS) method to couple drill string–wellbore contact with drilling fluid interactions. …”
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SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision by Yang Liu, Tianxing Yang, Liwei Tian, Jianbiao Pei

“…This method integrates three types of deep Q-learning networks (DQN, DDQN, and Dueling DDQN) and uses the Stochastic Gradient Descent (SGD) optimization algorithm to dynamically adjust the network weights. …”
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Deep deterministic policy gradient-based energy efficiency optimization algorithm for CR-NOMA by ZHANG Yun

“…Therefore, for CR-NOMA, deep deterministic policy gradient-based energy efficiency optimization (DPEE) algorithm was proposed. By jointly optimizing the transmission power and time slot splitting coefficient, the energy efficiency of sensor devices was improved. …”
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Route Optimization of Pipeline in Gas-Liquid Two-Phase Flow Based on Genetic Algorithm by Jun Zhou, Guangchuan Liang, Tao Deng, Jing Gong

“…This paper describes the problems in route optimization of two-phase pipelines. Combining the hydraulic calculation with route optimization theory, this paper establishes an automatic route optimization model and adopts the general genetic algorithm (gGA) and steady-state genetic algorithm (ssGA) to solve the model, respectively, gets the optimal route, and discusses the influence of parameters setting to the result. …”
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UWB Indoor Localization Based on Artificial Rabbit Optimization Algorithm and BP Neural Network by Chaochuan Jia, Can Tao, Ting Yang, Maosheng Fu, Xiancun Zhou, Zhendong Huang

“…The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. …”
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