Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM by Qingquan Lv, Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao, Haozhe Zhang

“…To improve short-term wind power prediction accuracy, this study constructs a hybrid particle swarm optimization (PSO)-CNN-LSTM model for seasonal forecasting. …”
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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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Comparative analysis of lumped and semi-distributed hydrological models for an upland watershed in Ethiopia by Gebiaw T. Ayele, Bofu Yu

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A Comprehensive Analysis of the Triple-Hybrid Metamodel of MLP-PSO-ARIMA for Forecasting the FDSD Index: A Case Study of Khuzestan Province by Mohammad Ansari Ghojghar

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Sparse support path generation for multi-axis curved layer fused filament fabrication by Tak Yu Lau, Dong He, Yamin Li, Yihe Wang, Danjie Bi, Lulu Huang, Pengcheng Hu, Kai Tang

“…Currently, most support generation algorithms are for the conventional 2.5D printing, which are not applicable to multi-axis printing. …”
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On differential privacy for federated learning in wireless systems with multiple base stations by Nima Tavangaran, Mingzhe Chen, Zhaohui Yang, José Mairton B. Da Silva Jr., H. Vincent Poor

“…To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. …”
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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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Potential of random forest machine learning algorithm for geological mapping using PALSAR and Sentinel-2A remote sensing data: A case study of Tsagaan-uul area, southern Mongolia by Munkhsuren Badrakh, Narantsetseg Tserendash, Erdenejargal Choindonjamts, Gáspár Albert

“…Geological mapping in remote and geologically complex regions can be substantially improved by integrating remote sensing data with machine learning algorithms. …”
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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…These optimization techniques aim to enhance the model's predictive performance by improving parameter tuning and model generalization. …”
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A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts by Yan Li

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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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The different member equivalent circulating density prediction model and drilling parameter optimization under narrow density window by Wanchun Zhao, Wanchun Zhao, Zhipeng Yang, Tingting Wang, Yan Zhou, Wei Song, Jingrun Li, Peihong Zhai

“…The model uses nonlinear regression algorithms to predict ECD values for different members. …”
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The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence by Yang Pu, Hao Li, Wenjia Hou, Xingchen Pan

“…To address this issue, this work proposes a hybrid optimization model combining transformer models and reinforcement learning algorithms, designed to optimize corporate strategic decision-making processes and improve competitiveness. …”
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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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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…Conclusion: VMAT-4P is identified as the most effective method for radiotherapy in left-sided breast cancer, providing an excellent balance between optimal target coverage and improved protection of surrounding OAR. …”
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Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold... by Zhixin Xu, Xiaoming Li, Bo Sun, Yueming Wen, Peipei Tang

“…It then employed the Wallacei_X plugin, which uses the NSGA-II algorithm for multi-objective genetic optimization (MOGO) to optimize five energy consumption and comfort objectives. …”
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Multi-objective data collecting strategies for wireless sensor network based on the time variable multi-salesman problem and genetic algorithm by Hao FENG, Lei LUO, Yong WANG, Miao YE

“…Comparing to the traditional data collecting method with data route,the technology of wireless mobile nodes has gradually became a new technique in the wireless sensor network.As the solution to the visiting order of the static nodes was an intrinsic NP-hard problem,a more general multi-objective data colleting strategies based on multi-mobile nodes was proposed.The proposed data collecting technique was abstracted as a model of time variable multiple traveling salesman problem.Belonging to a discrete optimal problem,the proposed model was solved by with a proposed hybrid genetic algorithm to determine the paths of the multi-mobile nodes.The convergence analysis of the proposed algorithm was given.With the experiment of open dataset,the proposed model based on the time variable multiple traveling salesman problem and the proposed hybrid genetic algorithm certify a certain improvement to the efficiency and real-time ability.…”
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Interacting Large Language Model Agents. Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

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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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On the machine learning algorithm combined evolutionary optimization to understand different tool designs’ wear mechanisms and other machinability metrics during dry turning of D2... by Muhammad Sana, Muhammad Umar Farooq, Sana Hassan, Anamta Khan

“…In this study, three-step novel modelling approach for optimal prediction of dry turning parameters is proposed. …”
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