A novel approach based on XGBoost classifier and Bayesian optimization for credit card fraud detection by Mohammed Tayebi, Said El Kafhali

“…This study proposes an enhanced XGBoost algorithm for detecting fraudulent transactions using an intelligent technique that tunes the hyperparameters of the algorithm through Bayesian optimization. …”
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A hybrid scheduling approach for mega event transportation: integrating harmony search and black widow optimization by Mohd Khaled Shambour, Esam Khan, Muhannad A. Abu-Hashem

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Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization by Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao, Fan Zhang

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Data Siting and Capacity Optimization of Photovoltaic–Storage–Charging Stations Considering Spatiotemporal Charging Demand by Dandan Hu, Doudou Yang, Zhi-Wei Liu

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Improved artificial bee colony algorithm for large scale colored bottleneck traveling salesman problem by Wenyong DONG, Xueshi DONG, Yufeng WANG

“…In the fields such as intelligent transport and multiple tasks cooperation, the model scale constructed by colored bottleneck traveling salesman problem (CBTSP) tends to large scale, and therefore it is necessary to study the large scale CBTSP and its algorithms. …”
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Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction by Yingying Song, Monchaya Chiangpradit, Piyapatr Busababodhin

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Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning by Xudong Zhao, Qingfen Ma, Jingru Li, Zhongye Wu, Hui Lu, Yang Xiong

“…To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. …”
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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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Research on tugboat scheduling optimization model considering the reliability of tugboat matching scheme by Yangjun Ren, Mengchi Li, Yushun Lei, Yan Zhou, Di Liu, Jianjun Tu, Botang Li

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Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector by Martina De Giovanni, Mariangela Lazoi, Romeo Bandinelli, Virginia Fani

“…AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. …”
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A Research Review of Order Allocation in Robotic Mobile Fulfillment Systems by LIU Zhaokai, MOU Jinrui, JIANG Ruyi, WANG Lin

“…Next, to further elucidate related solution methods, this paper introduces research progress in order allocation and multi-robot task scheduling from various perspectives, such as classical optimization methods, heuristic and meta-heuristic algorithms, rule-based strategies, simulation optimization algorithms, as well as artificial intelligence and machine learning techniques. …”
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ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions by Yirga Yayeh Munaye, Wasyihun Admass, Yenework Belayneh, Atinkut Molla, Mekete Asmare

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An Energy-Efficient Thrust Allocation Based on the Improved Dung Beetle Optimizer for the Dynamic Positioning System of Vessels by Yulong Tuo, Jianlong Lin, Zhouhua Peng, Yuanhui Wang, Shasha Wang

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A Virtual Power Plant-Integrated Proactive Voltage Regulation Framework for Urban Distribution Networks: Enhanced Termite Life Cycle Optimization Algorithm and Dynamic Coordination by Yonglin Li, Zhao Liu, Changtao Kan, Rongfei Qiao, Yue Yu, Changgang Li

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Accelerated Decentralized Load Balancing in Multi-Agent Networks by Victoria Erofeeva, Oleg Granichin, Elena Volodina

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Overhead line path planning based on deep reinforcement learning and geographical information system by Jiahui Chen, Yi Yang, Ling Peng, Lina Yang, Yinhui Han, Xingtong Ge

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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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The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020) by Parsa Ghannadi, Seyed Sina Kourehli, Seyedali Mirjalili

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Computation offloading scheme for RIS-empowered UAV edge network by Bin LI, Wenshuai LIU, Wancheng XIE, Zesong FEI

“…In order to address the challenge of low offloading rate caused by the obstacles blocking in the links between unmanned aerial vehicle (UAV) and ground users (GU) in urban scene, a partial task offloading scheme for UAV-enabled mobile edge computing with the aid of reconfigurable intelligence surface was proposed.A nonconvex and multivariable coupling stochastic optimization problem was formulated by the joint design of the computation task allocation, the transmit power of GU, the phase shift of RIS, UAV computation resource, and UAV trajectory, aiming at maximizing the minimum average data throughput of GU.By leveraging the properties of mathematical expectation, the stochastic optimization problem was transformed into a deterministic optimization problem.Then, the deterministic optimization problem was decomposed into three subproblems by using the block coordinate descent (BCD) algorithm.By introducing auxiliary variables, the nonconvex problems were transformed into convex optimization problems via the successive convex approximation and semidefinite relaxation, and then the approximate suboptimal solution of the original problem was obtained.The simulation results show that the proposed algorithm has good convergence performance and effectively improves the average data throughput of GU.…”
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Load frequency control in autonomous microgrids using hybrid Algorithm GWO-CS based fuzzy logic sliding mode controller by Abdessamade Bouaddi, Reda Rabeh, Mohammed Ferfra

“…The design of the FL-SMC controller is further enhanced by employing a hybrid optimization algorithm that combines the Gray Wolf Optimizer (GWO) and Cuckoo Search (CS). …”
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