Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). …”
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Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration by Jingshu Wang, Ping Li, Rutian Bi, Lishuai Xu, Peng He, Yingjie Zhao, Xuran Li

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Enhanced Prediction of California Bearing Ratio (CBR) Values in Geotechnical Engineering Using Decision Tree Algorithm and Meta-Heuristic Optimizations by Linda Davies, Dominik Jánošík

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

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

“… 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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Target image detection algorithm of complex road scene based on improved multi-scale adaptive feature fusion technology by Xu Zhaosheng, Liao Zhongming, Xiao Xiaoyong, Ahmad Suzana, Mat Diah Norizan, Ismail Azlan

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Short-term load estimation based on improved DBN-LSTM by Nan Dong, Yuwen Wu, Buyun Su, Zhanzhi Liu

“…The pruning algorithm is used to optimize the redundant structure of the model, reduce the complexity and training time of the model, and maintain or improve the forecasting accuracy. …”
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Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) by Norazman Shahar, Muhammad Amir As'Ari, Tan Tian Swee, Nurul Fathia Ghazali

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Optimization method of investment package based on Markowitz portfolio theory by A. D. Baydalin

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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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Surfactants Adsorption onto Algerian Rock Reservoir for Enhanced Oil Recovery Applications: Prediction and Optimization Using Design of Experiments, Artificial Neural Networks, and... by Kahina Imene Benramdane, Mohamed El Moundhir Hadji, Mohamed Khodja, Nadjib Drouiche, Bruno Grassl, Seif El Islam Lebouachera

“…A new data generation method based on a design of experiments (DOE) approach has been developed to improve the accuracy of adsorption modeling using artificial neural networks (ANNs). …”
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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

“…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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An Assessment of High-Order-Mode Analysis and Shape Optimization of Expansion Chamber Mufflers by Min-Chie CHIU, Ying-Chun CHANG

“…Using an eigenfunction (higher-order-mode analysis), a four-pole system matrix for evaluating acoustic performance (STL) is derived. To improve the acoustic performance of the expansion chamber muffler, three kinds of expansion chamber mufflers (KA-KC) with different acoustic mechanisms are introduced and optimized for a targeted tone using a genetic algorithm (GA). …”
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Research on improving the ranging accuracy of ships with stereo vision through Kalman filter optimization. by Zhongbo Peng, Jie Han, Liang Tong, Lumeng Wang, Dan Liu, Shijie Sun

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Comparative analysis of FACT devices for optimal improvement of power quality in unbalanced distribution systems by Ahmed M. Elkholy, Dmitry I. Panfilov, Ahmed E. ELGebaly

“…To improve network asymmetry, an optimization analysis is carried out to ascertain how each proposed device will work. …”
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Angular Random Walk Improvement of Resonator Fiber Optic Gyro by Optimizing Modulation Frequency by Wei Gao, Zhuo Wang, Guochen Wang, Weiqi Miao

“…In order to make this method effective, we use the particle swarm optimization algorithm to optimize the multi-parameter involved in ARW. …”
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Multiobjective optimization of suspension bridges via coupled modeling and dual population multiobjective particle swarm optimization by Peiling Yang, Jianhua Deng, Anli Wang

“…The algorithm divides the population into two parts, using the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO) for solving, with improvements to enhance the algorithm’s performance. …”
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Optimization of dynamic parameters of straddle monorail vehicle to improve pantograph-catenary contact quality by ZHANG Yuejun, YANG Zhen, DU Zixue, WEN Xiaoxia, XU Zhouzhou

“…In order to reduce the abrasion on the sliding block, a sensitivity analysis method was used in this paper to analyze the influence degree of vehicle dynamics parameters on the coupling force of pantograph and catenary based on the dynamic simulation analysis model of "vehicle-pantograph-catenary" system. The average value of the pantograph-catenary contact force was considered as the optimization objective, and dynamics parameters of the vehicle were optimized by non-dominated sorting genetic algorithm (NSGA-II). …”
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Modeling and Optimization of Co-TDMA in Next-Generation Wi-Fi Systems Based on Optimal Transportation Theory by Junxiong Zhang, Weimin Wu, Xiaohu Ge, Yingzhuang Liu

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

“…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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