Research of a spam filter based on improved naive Bayes algorithm by 曹翠玲, 王媛媛, 袁野, 赵国冬

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An Improved Shuffled Frog Leaping Algorithm for Electrical Resistivity Tomography Inversion by Fuyu Jiang, Likun Gao, Run Han, Minghui Dai, Haijun Chen, Jiong Ni, Yao Lei, Xiaoyu Xu, Sheng Zhang

“…Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. …”
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Inverse Kinematics: Identifying a Functional Model for Closed Trajectories Using a Metaheuristic Approach by Raúl López-Muñoz, Mario A. Lopez-Pacheco, Mario C. Maya-Rodriguez, Eduardo Vega-Alvarado, Leonel G. Corona-Ramírez

“…Additionally, a method to identify a functional model that describes the effector trajectories is introduced using the same optimization technique. …”
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Cable Force Optimization in Cable-Stayed Bridges Using Gaussian Process Regression and an Enhanced Whale Optimization Algorithm by Bing Tu, Pengtao Zhang, Shunyao Cai, Chongyuan Jiao

“…This study proposes an integrated framework combining Gaussian process regression (GPR) with an enhanced whale optimization algorithm improved by the Salp Swarm Algorithm (EWOSSA). …”
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AC Optimal Power Flow Problem Considering Wind Energy by an Improved Particle Swarm Optimization by Mohammad Reza Ansari, Hossein Ramzaninezhad

“…The proposed AC-OPF formulation includes the integer variables in addition to continuous variables and studies the effects of wind energy, transformer tap settings, and shunt capacitors on fuel cost, transmission losses as well as up and down spinning reserves. To solve the AC-OPF model, an Improved Particle Swarm Optimization (IPSO) is presented. …”
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Operation Optimization Strategy of Commercial Combined Electric Heating System Based on Particle Swarm Optimization Algorithm by WANG Qing, LI Congcong, WANG Pingxin, WU Qingqing, CAI Xiaoyu

“… In order to improve the energy efficiency of the electric heating system, a particle swarm optimization (PSO, Particle Swarm Optimization)-based operation optimization strategy for the direct storage combined electric heating system is proposed.A mathematical model of influencing factors inside and outside the walls of electric heating buildings is established, and the simulink toolbox in matlab is used to build the overall system under the premise of determining the quantity of electric heating.Combining demand response ideas, the objective function is to establish the minimum heating and electricity cost of the user, and different sub-modules are selected to form the control module to achieve simulation verification, and the inverse cosine method is used to update the improved particle swarm algorithm to update the learning factor to solve the set objective function.Finally, through a calculation example of electricity consumption data of an enterprise in Jinan, Shandong, comparing energy consumption and economy can be obtained: the total energy consumption throughout the day is lower than the actual energy consumption, and the electricity bill is reduced by 17.16% compared with the unoptimized time.…”
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Predicting the Compressive Strength of High-Performance Concrete utilizing Radial Basis Function Model integrating with Metaheuristic Algorithms by LiWei Hu

“…In addition, RBF is combined with the Sine Cosine Algorithm (SCA) and the African Vulture Optimization Algorithm (AVOA) to obtain the desired results close to the experimental values. …”
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PERFORMANCE OPTIMIZATION OF TRANSMISSION GEAR BASED ON OPTIMAL MODIFICATION DESIGN by HAN Wei, REN ZhiQun

“…At the same time,it can be proved that this method can effectively improve the meshing condition of gears and is an effective means to optimize the meshing performance of gears.…”
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Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm by Yonghui Duan, Chen Li, Xiang Wang, Yibin Guo, Hao Wang

“…Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. …”
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Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province by Yang Li, Guoen Zhou, Jiaqi Xue, Junwei Yang, Shi Yin

“…To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). …”
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Optimal Design of Multiband Microstrip Antennas by Self-Renewing Fitness Estimation of Particle Swarm Optimization Algorithm by Xiaohong Fan, Yubo Tian, Yi Zhao

“…In order to reduce the time of designing microstrip antenna, this paper proposes a self-renewing fitness estimation of particle swarm optimization algorithm (SFEPSO) to improve the design efficiency. …”
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Design of a Suspension Controller with Human Body Model for Ride Comfort Improvement and Motion Sickness Mitigation by Jinwoo Kim, Seongjin Yim

“…This paper presents a method to design a suspension controller with a human body model for ride comfort improvement and motion sickness mitigation. …”
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Optimization Study of Centrifugal Fan Volute Parameters based on Non-dominated Sorting Genetic Algorithm III Algorithm by J. L. Li, X. J. Wang, H. Gong, J. J. Wang

“…The BP neural network provided highly accurate fitting and predictions, yielding a reliable surrogate model. After optimization, the centrifugal fan’s Q increased by 2.29%, and η improved by 2.96%. …”
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Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning by Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram, Ehab Seif Ghith, Mehdi Tlija, Tai-hoon Kim, Imran Ashraf

“…The GA optimizes the number of layers, kernel size, learning rates, dropout rates, and batch sizes of the CNN model to improve the accuracy and performance of the model. …”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods by Tayfun Abut, Enver Salkım, Andreas Demosthenous

“…The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. …”
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A novel extraction model optimization with effective separation coefficient for rare earth extraction process using improve differential evolution by Fangping Xu, Hui Yang, Jianyong Zhu, Wenjia Chang

“…Taking into account the multi-modal and multi-variable characteristics of the optimized objective function, we put forth an enhanced version of the improved differential evolution algorithm, the Linear-Chaos and Two Mutation Strategies of Adaptive Differential Evolution (LCTADE)with Covariance Matrix and Cauchy Perturbation(CC-LCTADE). …”
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Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm by Mi Zhang, Guosheng Zhou, Bei Liu, Dajun Huang, Hao Yu, Li Mo

“…Validation on classical test functions and the Jiangpinghe River of the multi-timescale nested optimal scheduling model demonstrates that MPWOA exhibits faster convergence and stronger optimization capabilities and significantly improves power generation. …”
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A Study on Hysteresis Stiffness Model and Parameter Identification of Harmonic Gear Transmission based on Genetic Characteristic by Linfeng Qiu, Manyi Chen, Gang Song, Jie Zhang, Ran Yang, Han Zhang

“…Based on the experimental data，particle swarm optimization algorithm is used to identify the model parameters. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…Additionally, we developed a Multi-Objective Genetic Algorithm (MOGA)-enhanced RNN model to optimize hyperparameters and achieve accurate traffic speed predictions. …”
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