Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials by Jiahao Ye, Lirong Xie, Lan Ma, Yifan Bian, Chuanshi Cui

“…Firstly, to improve the accuracy and stability of wind-photovoltaic power forecasting, a novel multi-objective wind-photovoltaic forecasting model is proposed based on the Laguerre polynomial, pseudo-inverse learning, and hybrid multi-objective Runge-Kutta algorithm (HMORUN). …”
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Modeling and Solving the Multi-Objective Vehicle Routing Problem with Soft and Fuzzy Time Windows by Ailing Chen, Tianao Li

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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…Abstract This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. …”
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. …”
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A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration by Fadi Agha Kassab, Rusber Rodriguez, Berk Celik, Fabrice Locment, Manuela Sechilariu

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Hierarchical guided manta ray foraging optimization for global continuous optimization problems and parameter estimation of solar photovoltaic models by Zhentao Tang, Kaiyu Wang, Lan Zhuang, Mingxin Zhu, Yongxuan Yao, Huiqin Chen, Jing Li, Xiaoxiang Wu, Shangce Gao

“…Abstract The manta ray foraging optimization (MRFO) algorithm has gained widespread application in engineering problems due to its notable performance and low computational cost. …”
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Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision by Jui-Sheng Chou, Chi-Yun Liu, Pin-Jun Guo

“…This system uses UAVs to capture high-resolution images, which are then processed by the You Only Look Once (YOLO) models for instance segmentation. The YOLOv7 model, fine-tuned with the Pilgrimage Walk Optimization (PWO)-Lite algorithm, achieved the highest accuracy, recording a 65.6 % mAP50 on the testing set. …”
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Charge and discharge scheduling method for large-scale electric vehicles in V2G mode via MLGCSO by Songling Pang, Kaidi Fan, Meiyi Huo

“…Compared with traditional methods, the diversity and convergence of particle swarm learning are enhanced, and the optimization performance is improved. Simulation results indicate that when compared with three state-of-the-art optimizers, the optimization accuracy of the proposed algorithm is increased by at least 34% and the total cost is reduced by 3.14% and 1.62% respectively, demonstrating that the MLGCSO exhibits high optimization performance and remarkable optimization effects.…”
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Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition by Hang Yu, Junbo Lei, Pengfei Lin, Tao Zhang, Hailong Liu, Huilin Lai, Lindong Lai, Bowen Zhao, Bo Wu

“…This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. …”
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An improved deep learning model for soybean future price prediction with hybrid data preprocessing strategy by Dingya CHEN, Hui LIU, Yanfei LI, Zhu DUAN

“…Finally, the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm. In the deep learning prediction stage, a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results. …”
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Optimizing Berth Allocation for Maritime Autonomous Surface Ships (MASSs) in the Context of Mixed Operation Scenarios by Lixin Shen, Xueting Shu, Chengcheng Li, Tomaž Kramberger, Xiaoguang Li, Lixin Jiang

“…A large-scale simulation of the mixed-type berth allocation model is carried out using an improved simulated annealing algorithm. …”
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Safety Status Prediction Model of Transmission Tower Based on Improved Coati Optimization-Based Support Vector Machine by Xinxi Gong, Yaozhong Zhu, Yanhai Wang, Enyang Li, Yuhao Zhang, Zilong Zhang

“…Subsequently, we employ the improved coati optimization algorithm (ICOA) to refine the penalty parameters and kernel function of the support vector machine (SVM), thereby developing the safety state prediction model for the transmission tower. …”
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Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm by Youtao Xue, Yuxin Yang, Shaobo Yao, Wenwen Zhao, Lihua Chen

“…This study introduces an SBO framework tailored for Gaussian process regression models, featuring an automatic kernel construction algorithm coupled with a beam search strategy (AKC-GPR). …”
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ALGORITHM FOR PLANNING PROJECTS by Y. N. Kondrashov, M. O. Chashchin

“…The proposed algorithm uses a modified resource network model that allows using regular optimization algorithms in planning procedures, and provides flexibility in establishing criteria for planned decision. …”
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Ship Formation and Route Optimization Design Based on Improved PSO and D-P Algorithm by Peilong Xu, Dan Lan, Haolin Yang, Shengtian Zhang, Hyeonseok Kim, Incheol Shin

“…Based on this, a ship formation model combining improved particle swarm optimization algorithm and a generative route optimization method based on improved Douglas-Peucker algorithm are proposed. …”
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Research on switch migration method based on minimum cost path by Yingxu LAI, Yewei PU, Jing LIU

“…In order to protect the controller,especially the controller in backbone network,from security threats and attacks,improve the security of the software-defined network (SDN) control plane,a switch migration algorithm based on minimum cost path was proposed.A load prediction module was added to the migration model,which executed a controller load prediction algorithm to obtain a load prediction matrix,and then a migration-target controller set was determined according to the load prediction matrix.The improved Dijkstra algorithm was used to determine the minimum cost path.According to the load state of the controller and the traffic priority of the switch to be migrated,the optimal migration switch set was determined.The problem of isolated nodes was solved that may occur during the migration process.The experimental results show that the migration timing of the algorithm is more reasonable,the selection of the migration controller and the target controller is more reasonable,the load balancing of the control plane is realized,the number of migrations and cost are reduced,and the performance of the controller is improved.…”
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Multijunction solar cell parameter estimation based on metaheuristic algorithms by Marwa M. Elzalabani, Doaa M. Atia, Aref Y. Eliwa, Belal A. Abou Zalam, Mahmoud S. AbouOmar

“…In addition, The Newton-Raphson Method (NRM) hybrid with meta-heuristic algorithms, or MOAs, is used to improve solution estimates, accelerate convergence rates, reduce computational costs, and enhance robustness. …”
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Methods and Algorithms for Predictive Analytics of Time Series in Energy Consumption by Aleksandr Karmanov

“…States must minimize carbon dioxide emissions and businesses must optimize their energy costs. In this regard, improving energy efficiency plays a key role in solving the climate crisis and reducing costs for businesses. …”
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Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm by Yongjin Lu, Kai Li, Rui Lin, Yunlong Wang, Hairong Han

“…The improved algorithm has better global search ability, higher solution stability, and faster convergence speed than the standard grey wolf optimization algorithm. …”
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An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis by Xiaofeng Lv, Deyun Zhou, Ling Ma, Yuyuan Zhang, Yongchuan Tang

“…The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. …”
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