Automatic strip layout design in progressive dies using the grouping genetic algorithm by Mehran Afshari, Behrooz Arezoo

“…In the present study, a new method is presented for the automatic strip layout design for progressive dies using the Grouping Genetic Algorithm. A two-objective function is used in the optimization process. …”
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Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm by Hua Xu, Zongkai Guo, Yu Cao, Xu Cheng, Qiong Zhang, Dan Chen

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Optimizing power network expansion with pumped hydro energy storage using a multi-objective enhanced spider wasp optimizer approach by Mohamed M. Refaat, Saad F. Al-Gahtani, Hussain Bassi, Ziad M. Ali, Shady H. E. Abdel Aleem

“…The problem is framed as a comprehensive multi-objective optimization model designed to minimize the cost of network expansion while maximizing the benefits of integrating various PHES technologies. …”
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Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming by Jing Zhang, Li Lei, Xin Feng

“…Second, a potential game model is proposed as a joint method for optimizing the collaborative parameters of the cooperative nodes and their energy consumption balancing features. …”
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Multi-Task Perception Algorithm for Rail Transit Scenarios Based on Triplet Attention by GAO Rui, XIONG Yanping, WEI Chenfeng, XIE Guotao, GAO Ming

“…Through validation using datasets from actual scenes, the model exhibited significant improvements in small object detection, detection speed, and environmental adaptability. …”
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Storage Assignment Optimization in Robotic Mobile Fulfillment Systems by Ruiping Yuan, Juntao Li, Wei Wang, Jiangtao Dou, Luke Pan

“…A two-stage hybrid algorithm combining greedy algorithm and improved simulated annealing is designed to solve the model. …”
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Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
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A hybrid Spatial Ant Lion optimization and utilitarian data transmission approach for intelligent optimization for energy-efficient wireless sensor networks by Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj, C N Vanitha, Md Shohel Sayeed

“…Results demonstrate that the proposed model achieved a 1.86% increase in throughput, a 2.56% reduction in delay, and a 3.18% improvement in energy efficiency when compared to existing schemes such as PSO, ALO, and SA-AOA.…”
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Research of Online Error Prediction and Parallel Algorithm for Voltage Transformer in Smart Grid by Xin Xianfeng, Chen Rongye, Lin Cong, Liu Qingchan, He Zhaolei, Li Tengbin, Yang Guangrun, Kochan Orest, Karpa Mykhailo

“…This article mainly focuses on the online error prediction and the parallelization method of voltage transformers. First, an optimized multi-layer perceptron model based on the sparrow search algorithm (SSA) is proposed. …”
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KAB: A new k-anonymity approach based on black hole algorithm by Lynda Kacha, Abdelhafid Zitouni, Mahieddine Djoudi

“…Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. …”
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Research on Flexible Job Shop Scheduling Method for Agricultural Equipment Considering Multi-Resource Constraints by Zhangliang Wei, Zipeng Yu, Renzhong Niu, Qilong Zhao, Zhigang Li

“…To tackle this complex problem, a Multi-Objective Discrete Grey Wolf Optimization (MODGWO) algorithm is proposed. The MODGWO algorithm integrates a hybrid initialization strategy and a multi-neighborhood local search to effectively balance the exploration and exploitation capabilities. …”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

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Machine learning for brain tumor classification: evaluating feature extraction and algorithm efficiency by Krishan Kumar, Kiran Jyoti, Krishan Kumar

“…However, performance varied with KNN, Naive Bayes, and Decision Tree, highlighting the importance of tailored approaches for optimal classification accuracy. Further optimization and experimentation are crucial for improving algorithm performance in real-world applications of brain tumor classification. …”
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Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks by Yuzhu Luo, Jiarong Wang, Ming Wei

“…From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. …”
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Wide area cooperative resource allocation algorithm for shortwave communication access network by Guojun LI, Xu HOU, Changrong YE, Yiping LUO

“…Aiming at the problem that short-wave point-to-point communication was not reliable enough to meet the actual needs, a wide area cooperative resource allocation algorithm based on shortwave communication access network was proposed.The reliability of the shortwave communication was improved by invoking multiple stations to coordinately guarantee users from different channels.The resource allocation problem was decomposed into two sub-problems of channel and station matching and station allocation.Firstly, the maximum expected success rate matching model was established to match the channel and the station, and the stations allocated to the channel were combined into different guarantee schemes.Then, the fuzzy analytic hierarchy process and the entropy weight method were used to obtain the subjective and objective scores of the scheme, and the optimal scheme was selected through fusion of evidence reasoning.The results show that the proposed algorithm can effectively improve the success rate of shortwave communication and has good adaptability.…”
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Application of adaptive artificial bee colony algorithm in environmental and economic dispatching management by Zhang Longyue, Zhang Haoyan

“…With the increasingly serious global environmental and energy issues, more countries are using environmental and economic dispatch (EED) models to optimize power systems. To better optimize the power system, an adaptive artificial bee colony (ABC) algorithm based on memory feedback mechanism was put forward to address the EED model, and the adaptive algorithm was used to adaptively adjust the population size. …”
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Theoretical and Experimental Study of Energy-Harvesting and Movement-Sensing Solutions in Pneumatic Systems by Monica Tiboni, Federico Scassola, Alessandro Zanacchi, Marco Ghidini

“…Following confirmation of the adapted model’s validity, the authors propose using genetic algorithms to design an optimized mechanical energy harvester. …”
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Artificial Intelligence Algorithms for Hybrid Electric Powertrain System Control: A Review by Dawei Zhong, Bolan Liu, Liang Liu, Wenhao Fan, Jingxian Tang

“…In recent years, artificial intelligence algorithms have provided new solutions for the intelligent control of hybrid electric powertrain systems with their powerful nonlinear modeling capabilities, data-driven optimization, and adaptive learning capabilities. …”
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A machine learning algorithm for personalized healthy and sustainable grocery product recommendations by Laura Z.H. Jansen, Kwabena E. Bennin

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Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users by Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel

“…Hyper-parameter tuning using any HPO algorithm/sampler improved model discrimination (AUC=0.84), resulted in models with near perfect calibration, and consistently identified features predictive of high-need high-cost health care users. …”
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