Machine learning-based coalbed methane well production prediction and fracturing parameter optimization by HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan

“…The model employs a random forest algorithm integrated with a multi-task learning strategy and utilizes a particle swarm optimization (PSO) algorithm to optimize fracturing parameters. …”
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Bacterial foraging optimization building block distribution algorithm based dynamic allocation in multiple robotic system by P. Anand Raj, S. Palanimurugan, S. Senthilkumar

“…The proposed algorithm addresses the limitations of existing methods by integrating probabilistic modeling with multivariate factorization to optimize task allocation. …”
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Multi-objective: hybrid particle swarm optimization with firefly algorithm for feature selection with Leaky ReLU by Ashish Kumar Singh, Anoj Kumar

“…Abstract High-dimensional datasets often pose challenges due to the presence of numerous irrelevant and redundant features, which can compromise the performance of machine learning models. This study proposes a novel optimization algorithm, LR-GPSOFA, designed to improve feature selection by enhancing computational efficiency and classification accuracy. …”
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A Novel Conjugate Gradient Algorithm for Unconstrained Optimization and Its Application in COVID-19 Data Parameterization by Basim A. Hassan, Ibrahim Sulaiman Mohammed, Alaa Luqman Ibrahim, Faisal Falah Aiwa

“… To solve unconstrained optimization problems, this study proposes a conjugate gradient (CG) algorithm that satisfies both the convergence and descent conditions. …”
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Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural... by Messaouda Boumaaza, Ahmed Belaadi, Hassan Alshahrani, Ibrahim M. H. Alshaikh, Djamel Ghernaout

“…To model and optimize the mechanical behavior, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Multi-Criteria Decision-Making (MCDM) method based on TOPSIS were applied. …”
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Submarine Cable Vibration Signal Recognition Based on Sparrow Search Algorithm Optimized Support Vector Machine by GUO Jiaxing, QIAN Junxia, LIU Rui, MA Hongjuan

“…Online status monitoring and fault recognition of photoelectric composite submarine cables (hereinafter referred to as submarine cables) offers an effective approach for early warning of faults in submarine cables. In order to improve the speed and accuracy of this practice, this paper proposes a submarine cable vibration signal recognition method based on the sparrow search algorithm (SSA) optimized support vector machine (SSA-SVM). …”
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Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment by Wentian Shang, Jinzhang Jia

“…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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A Cloud Vertical Structure Optimization Algorithm Combining FY-4A and DSCOVR Satellite Data by Zhuowen Zheng, Jie Yang, Taotao Lv, Yulu Yi, Zhiyong Lin, Jiaxin Dong, Siwei Li

“…To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. …”
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Multipath Branch Model-Aided Differential Evolution Algorithm Based on Regional Error by Yang Liu, Yi-Xuan Zhang, Zhong-Chao Lin, Yu-Jia Zou

“…In order to overcome the defect of a single model in the adaptive antenna optimization problem and improving the efficiency and accuracy of the model, a method called multipath branching model-aided differential evolution algorithm analysis (MMDEA-RE) is proposed. …”
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Multi class aerial image classification in UAV networks employing Snake Optimization Algorithm with Deep Learning by Alanoud Al Mazroa, Nuha Alruwais, Muhammad Kashif Saeed, Kamal M. Othman, Randa Allafi, Ahmed S. Salama

“…Furthermore, the integration of Snake Optimization algorithms assists in fine-tuning the classification process, improving accuracy. …”
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MULTI-OBJECTIVE OPTIMIZATION DESIGN OF ROADHEADER’S CUTTING HEAD BASED ON THE GA by ZHAO LiJuan, FAN JiaYi

“…In order to improve the dynamic reliability of the roadheader,the roadheader’s rigid-flexible coupled model was established based on virtual prototyp,dynamic reliability analysis was done on the rotary table of different structural parameters,evaluation function was established based on mechanical optimization design theory,the function’s design variables was half cone angle,helix Angle and cutting line spacing, the function’s objective function was the minimization of maximum equivalent stress as the objective function.The cutting head’s optimal structural parameters was obtained by genetic algorithm.Based on the cutting productivity and rotary table equivalent stress, the optimal yawing speed was obtained by optimized multi-objective.The results shows that after two optimizations,rotary table maximum stress is decreased 18.495 MPa,the fatigue life is improved from 3.067 E4 to 3.326 E6,productivity is improved 18.6 t/h,prediction error is less than 1.3%,meet the design requirement.This method provides data support for the structure and kinematic parameters of cutting head,provides a new method for the optimization design of heavy complicated mechanical equipment.…”
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A dual-layer planning method based on improved MOPSO for distribution networks considering source–load temporal uncertainty by Guilian Wu, Guilian Wu, Jia Lin, Jinlin Liao

“…Third, an improved multi-objective particle swarm optimization (MOPSO) algorithm with adaptive inertia weights accelerates the convergence by 25%. …”
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Research on multi-objective optimization method for bullet full trajectory based on SA-PSO hybrid algorithm by HU Zhenchao, CUI Xiao, XU Xiao, LU Dabin, ZHANG Huisheng

“…The results demonstrate that this approach converges to the optimal solution more efficiently compared to traditional algorithms. …”
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Research on E-Commerce Inventory Sales Forecasting Model Based on ARIMA and LSTM Algorithm by Chenyang Wang, Junsheng Wang

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Design of dual-layer heater based on genetic algorithm to optimize magnetic field gradient in vapor cell by Zhicheng Tan, Jing Zhu, Yanyan Liu, Siyang Lu, Lianqing Zhu

“…The study analyzed the influence of key parameters of the resistive wire, such as wire width, thickness, and spacing, on magnetic noise generation in the three-dimensional model of the heater. The parameter combinations were then optimized synchronously using genetic algorithms to reduce the magnetic field gradient in the vapor cell region and enhance the magnetic noise self-suppression capability of the heater. …”
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Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters by Hauwau Abdulrahman Aliyu, Ibrahim Olawale Muritala, Habeeb Bello-Salau, Salisu Mohammed, Adeiza James Onumanyi, Ore-Ofe Ajayi

“…Diabetes mellitus poses a global health concern, prompting the development of machine learning algorithms designed to construct a model for the accurate classification of patients, enabling precise diagnoses and early-stage treatment. …”
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Modelling and optimization of TPMLMs with slotted stators based on Bayesian DNN by Tao Wu, Peipei Dai, Kai Zhu, Yachao Zhu

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Identification of polynomial models of static load characteristics based on passive experiment results by N. L. Batseva, A. K. Zhuykov

“…In the paper, the technique based on the initial identification of the linear model, defined by EM-algorithm, and continued by the Lagrange multiplier method optimization with iterations by the Newton method is suggested.Results and discussion. …”
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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models by Adel Zga, Farouq Zitouni, Saad Harous, Karam Sallam, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

“…The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. …”
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PCA-FSA-MLR Model and Its Application in Runoff Forecast by GUO Cunwen, CUI Dongwen

“…To improve the accuracy of runoff forecast,and establish a runoff forecast model combining principal component analysis (PCA),future search algorithm (FSA),and multiple linear regression (MLR),this paper reduces the dimensionality of the sample data by PCA,selects 8 standard test functions and simulates and verifies FSA under different dimensional conditions,optimizes MLR constant terms and partial regression coefficients by FSA,proposes a PCA-FSA-MLR runoff forecast model,constructs PCA-LS-MLR,PCA-FSA-SVM,and PCA-SVM models with dimensionality reduction processing by PCA and FSA-MLR,LS-MLR,FSA-SVM,and SVM without dimensionality reduction processing as a comparison model,and verifies each model through forecasting the annual runoff and monthly runoff in December of Longtan station in Yunnan Province.The results show that:①FSA has better optimization accuracy and global extremum search ability under different dimensional conditions;②The average absolute relative error of the annual runoff and monthly runoff in December of Longtan station through PCA-FSA-MLR model are 1.63% and 3.91% respectively,and its forecast accuracy is better than the other 7 models,with higher forecast accuracy and stronger generalization ability;③For the same model,the forecast accuracy after dimensionality reduction processing by PCA is better than that without dimensionality reduction processing,so the data dimensionality reduction by PCA is helpful to improve the forecast accuracy of models.…”
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