Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data by Zhiwei Xun, Farag M. A. Altalbawy, Prakash Kanjariya, R. Manjunatha, Debasish Shit, M. Nirmala, Ajay Sharma, Sarbeswara Hota, Shirin Shomurotova, Fadhil Faez Sead, Hojjat Abbasi, Mohammad Mahtab Alam

“…Gradient boosting machine (GBM) models were optimized using advanced algorithms like self-adaptive differential evolution (SADE), evolution strategy (ES), Bayesian probability improvement (BPI), and Batch Bayesian optimization (BBO). …”
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An Intelligent Inspection and Scheduling Algorithm for Integrated Pipe Gallery Based on Cloud Robot by ZHOU Xiwei, SHANG Xiao, SHANG Xinjuan, YAN Maode, WANG Fafa

“…In order to solve the problem of multiple iterations and poor real-time performance, it proposed an improved adaptive weight particle swarm optimization-genetic hybrid optimization algorithm combined with particle swarm optimization and genetic algorithm. …”
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Metaheuristics in automated machine learning: Strategies for optimization by Francesco Zito, El-Ghazali Talbi, Claudia Cavallaro, Vincenzo Cutello, Mario Pavone

“…We examine various metaheuristic algorithms employed and, in particular, their effectiveness in improving model performance across diverse applications. …”
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Development of scientific and methodological approaches to improve the efficiency of quarry transport operation by A. N. Novikov, I. A. Novikov, N. A. Zagorodnij, A. S. Semykina

“…It is concluded that it is necessary to develop scientific and methodological approaches to improve the operation of open pit transport. An algorithm for the operation of open pit transport in a simulation model has been developed, which allows taking into account the influence of the control and distribution system of dump trucks on the number of excavators and the timely delivery of dump trucks to storage points. …”
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Dual-Closed-Loop Control System for Polysilicon Reduction Furnace Power Supply Based on Hysteresis PID and Predictive Control by Shihao Li, Tiejun Zeng, Shan Jian, Guiping Cui, Ziwen Che, Genghong Lin, Zeyu Yan

“…Meanwhile, the outer loop employs a hybrid MFAC-based improved PID algorithm to optimize the temperature tracking performance, achieving precise temperature control even in the presence of system uncertainties. …”
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ISAR High Resolution Imaging Algorithm Based on Weighted Adaptive Mixed Norm by Q. Zhang, Q. Q. Chen, G. Xu, J. R. Chi

“…The optimization model takes advantage of mixed norm to achieve fast convergence in the operation, and adopts conjugate gradient descent method and fast Fourier transform operation in the solution, which simplifies the solving process of the optimization problem and improves the operation efficiency of the algorithm. …”
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Flexible scheduling strategy for power systems considering source-load uncertainty by Qunmin YAN, Xiaoyu REN, Xiao SONG, Mengjue ZHAO, Chen AN

“…The two-stage robust model is transformed into relatively independent main problems and sub-problems，and the column constraint generation （C&CG） algorithm and strong dyadic theory are adopted to iterate repeatedly to approximate the optimal solution. …”
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Modelo matemático y algoritmo de solución de un flujo con tiempos dependientes de la secuencia // A mathematical model and solution algorithm to a flow shop task with times dependi... by A. Fiol-Zulueta, J. Arzola-Ruíz, A. da Silva-Carvalho

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Energy and channel transmission management algorithm for resource harvesting body area networks by Zhigang Chen, Lin Guo, Deyu Zhang, Xuehan Chen

“…Based on the proposed model, we formulate an optimization problem of system utility maximization. …”
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A Ｒeinforcement Learning of Cloud Ｒesource Scheduling Algorithm Based on Adaptive Weight by LI Cheng-yan, SUN Wei, TANG Li-min

“…We considered the cloud computing resource scheduling problem，and proposed a multi-objective optimization mathematical model to optimize task completion time and running cost simultaneously. …”
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Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

“…To further improve predictive accuracy, the TOTO metaheuristic algorithm is applied for model fine-tuning. …”
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Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction by Pin‐Chun Huang

“…The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. …”
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Deep learning-based feature selection for detection of autism spectrum disorder by Ibrahim Nafisah, Nermine Mahmoud, Ahmed A. Ewees, Mohamed G. Khattap, Abdelghani Dahou, Safar M. Alghamdi, Ibrahim A. Fares, Mohammed Azmi Al-Betar, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz, Mohamed Abd Elaziz

“…Feature selection is enhanced through an optimized Hiking Optimization Algorithm (HOA) that integrates DynamicOpposites Learning (DOL) and Double Attractors to improve convergence toward the optimal subset of features.ResultsThe proposed model is evaluated using multiple ASD datasets. …”
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Progressive filling partitioning and mapping algorithm for Spark based on allocation fitness degree by Chen BIAN, Jiong1 YU, Wei-rong XIU, Bin LIAO, Chang-tian YING, Yu-rong QIAN

“…The job execution mechanism of Spark was analyzed,task efficiency model and Shuffle model were established,then allocation fitness degree (AFD) was defined and the optimization goal was put forward.On the basis of the model definition,the progressive filling partitioning and mapping algorithm (PFPM) was proposed.PFPM established the data distribution scheme adapting Reducers’ computing ability to decrease synchronous latency during Shuffle process and increase cluster the computing efficiency.The experiments demonstrate that PFPM could improve the rationality of workload distribution in Shuffle and optimize the execution efficiency of Spark.…”
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Adaptive Controller Design for Improving Helicopter Flying Qualities by Wei Wu

“…In online system identification module, a recursive extended least squares algorithm is established to identify the augmented linear flight dynamics model which is composed of helicopter model and unideal noise model. …”
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Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data by Simone A. Ludwig

“…It involves selecting a subset of relevant features for use in model construction. Feature selection helps in improving model performance by reducing overfitting, enhancing generalization, and decreasing computational cost. …”
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Optimization Scheduling of Multiple Heterogeneous Energy Sources by Ying Zhao, Zhiwen Yu, Xiaobin Wang, Jianlin Tang, Xiaoming Lin, Fan Zhang, Bin Qian

“…The study summarizes the mainstream mathematical modeling and optimization algorithms, intelligent optimization techniques, and real-time data processing technologies. …”
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Research on International Law Data Integrity Guarantee Based on Antiterrorism Prediction Algorithm by Huang Ru Qing

“…In order to improve the quality of international law data, this paper designs a method to ensure the integrity of international law data based on an antiterrorism prediction algorithm. …”
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Algorithm study of digital HPA predistortion using one novel memory type BP neural network by Chun-hui HUANG, Yong-jie WEN

“…Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations,a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier.The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network.Due to the new parameters w⁰in the novel NN model,the modified formulas of LM algorithm were provided.Next，in order to eliminate the over-fitting of LM algorithm,the Bayesian regularization algorithm was applied to the predistortion system.Additionally,the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA.Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB.Moreover,with the mean square error less than 10⁻⁶,the coefficient of network for FIR-NLNNN is about half of that for RVTDNN.Similarly,the times of multiplication and addition in the iterative process of FIR-NLNNN are about 25% of that for RVTDNN.…”
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An Enhanced Interval Type-2 Fuzzy C-Means Algorithm for Fuzzy Time Series Forecasting of Vegetation Dynamics: A Case Study from the Aksu Region, Xinjiang, China by Yongqi Chen, Li Liu, Jinhua Cao, Kexin Wang, Shengyang Li, Yue Yin

“…Fuzzy time series (FTS) prediction models based on the Fuzzy C-Means (FCM) clustering algorithm address some of these uncertainties by enabling soft partitioning through membership functions. …”
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