An improved catastrophe progression method based on HMSLGWO–AHP for grouting quality assessment by Yushan Zhu, Zhu Yang, Ning Li, Jian Huang

“…Subsequently, the analytic hierarchy process (AHP) method improved by the hierarchical multi‐strategy learning gray wolf optimization (HMSLGWO) algorithm is employed to determine the relative significance of indices, in which, the HMSLGWO algorithm, augmented by Gaussian mixture model clustering and multi‐strategy learning, optimizes the consistency of the AHP judgment matrix. …”
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Improved active vibration control of a cantilever beam using MFC actuators with Hammerstein model-based hysteresis modeling and VSS-FxLMS control algorithm by Hafiz Muhammad Umar, Lei Zhang, Ruichen Yu, Zhiyuan Gao

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Optimal Decision-making Model for Power Grid Maintenance Scheduling Considering Comprehensive Supply-Demand Factors by Hui LIU, Qianjun JIANG, Qianjin GUI, Lei WANG, Hongqiang TIAN, Jingjing WANG, Hejun YANG

“…Thirdly, a fitness optimization model is constructed based on the penalty function, and the genetic algorithm is used to solve the optimal outage decision-making problem. …”
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Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network by Binjie Li, Kuan Shen, Zhilong Song, Binghai Lyu, Wenhong Zhao

“…The improved genetic algorithm-optimized backpropagation (GA-BP) neural network model incorporates a dynamic nonlinear probability adjustment mechanism and a fitness calibration mechanism. …”
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A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems by Cornelius Rüther, Julia Rieck

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Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm by Jiansha Lu, Jiarui Zhang, Jun Cao, Xuesong Xu, Yiping Shao, Zhenbo Cheng

“…In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. …”
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Application research on classification and integration model of innovation and entrepreneurship education resources based on GNN-PSO algorithm by Yongjian Dong

“…The experimental results confirm that the classification and integration model of innovation and entrepreneurship education resources based on the GNN-PSO algorithm improves classification accuracy and optimizes the resource integration process, providing strong support for the development of innovation and entrepreneurship education.…”
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An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network by Xiaofeng Yang, Shousheng Zhao, Kangyi Li, Wenjin Chen, Si Zhang, Jingwei Chen

“…Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. …”
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Simulation Study of New Buckling Restraint Bracing Based on Bayesian Optimization Algorithm to Identify the Parameters of Modified Bouc–Wen Model by Lu Zhao, Tiangang Tu, Sunwei Ding, Xinqiao Liu

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System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario by Wenhao Luo, Yuan Zeng, Ximeng Zheng, Lingyan Zha, Weicheng Cai, Qing Wang, Jingjin Zhang

“…Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. …”
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Predicting Student Performance through Machine Learning Methods: Naive Bayesian Classifier by Chenyang Li, Qianzhi Zhou, Luming Du, Sihan Zhang

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Squirrel search algorithm-support vector machine: Assessing civil engineering budgeting course using an SSA-optimized SVM model by He Yanqing, Shi Ling, Yao Xiaoqin, Zhang Haojie, Al-Barakati Abdullah A.

“…The above results reveal that the proposed optimization algorithm and course evaluation model have good performance. …”
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Developing an Optimized Energy-Efficient Sustainable Building Design Model in an Arid and Semi-Arid Region: A Genetic Algorithm Approach by Ahmad Walid Ayoobi, Mehmet Inceoğlu

“…A comprehensive analysis and optimization model was developed using genetic algorithms to individually optimize various sustainable strategies. …”
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Analyzing social psychological impact on emotional expression through peer communication using crayfish optimization algorithm with deep learning model by Umkalthoom Alzubaidi

“…Finally, the crayfish optimization algorithm (COA) adjusts the VAE model’s hyperparameter values, improving classification. …”
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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep learning model with optimization algorithms by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Monir Abdullah, Samah Al Zanin

“…This manuscript presents an Enhance Malicious Domain Detection Using an Attention-Based Deep Learning Model with Optimization Algorithms (EMDD-ADLMOA) technique. …”
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An Optimal Longitudinal Control Strategy of Platoons Using Improved Particle Swarm Optimization by Zhizhou Wu, Zhibo Gao, Wei Hao, Jiaqi Ma

“…An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. …”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Pa... by Vahe S. Panossian, Yu Ma, Bolin Song, Jefferson A. Proaño-Zamudio, Veerle P. C. van Zon, Ikemsinachi C. Nzenwa, Azadeh Tabari, George C. Velmahos, Haytham M. A. Kaafarani, Dimitris Bertsimas, Dania Daye

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Based on the improved SCGM(1,1)c and WIV rainfall landslide susceptible area prediction model by Qian Zhang, Shujie Cao, Yanliang Du, MingYuan Du, Yixuan Zhao, Yaoqi Nie

“…On the basis of the single factor system cloud grey model (SCGM (1,1)c), an improved SCGM (1,1)c model is proposed based on Markov prediction theory and CS algorithm optimization to predict rainfall. …”
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IPO: An Improved Parrot Optimizer for Global Optimization and Multilayer Perceptron Classification Problems by Fang Li, Congteng Dai, Abdelazim G. Hussien, Rong Zheng

“…The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. …”
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