Short-Term Traffic Flow Forecasting Method Based on LSSVM Model Optimized by GA-PSO Hybrid Algorithm by Qichun Bing, Dayi Qu, Xiufeng Chen, Fuquan Pan, Jinli Wei

“…Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow forecasting method based on LSSVM model optimized by GA-PSO hybrid algorithm is put forward. …”
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A Unique Bifuzzy Manufacturing Service Composition Model Using an Extended Teaching-Learning-Based Optimization Algorithm by Yushu Yang, Jie Lin, Zijuan Hu

“…Next, we address the multi-objective optimization issue through the application of extended teaching-learning-based optimization (ETLBO) algorithm. …”
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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

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Integrated Modeling and Optimal Operation Strategy of Building Cooling System Combining the Standardized Thermal Resistance and Genetic Algorithm by Liang Tian, Bohong Lai, Tianzhen Yang, Xingce Wang, Junhong Hao, Kaicheng Liu

“…ABSTRACT Integrated modeling and operation optimization of building energy systems is significant for improving the energy utilization efficiency and reducing carbon emission. …”
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Modeling and Solving the Knapsack Problem with a Multi-Objective Equilibrium Optimizer Algorithm Based on Weighted Congestion Distance by Ziqian Wang, Xin Huang, Yan Zhang, Danju Lv, Wei Li, Zhicheng Zhu, Jian’e Dong

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A Novel Prediction Model for the Sales Cycle of Second-Hand Houses Based on the Hybrid Kernel Extreme Learning Machine Optimized Using the Improved Crested Porcupine Optimizer by Bo Yu, Deng Yan, Han Wu, Junwu Wang, Siyu Chen

“…For this reason, this paper develops a prediction model of the second-hand housing sales cycle based on the hybrid kernel extreme learning machine (HKELM) optimized using the Improved Crested Porcupine Optimizer (CPO), which has achieved rapid and accurate prediction. …”
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An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm by Wei Chen, Qike Cao, Bingyu Cao, Bo Jin

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Bio inspired optimization techniques for disease detection in deep learning systems by A. Ashwini, Vanajaroselin Chirchi, S. Balasubramaniam, Mohd Asif Shah

“…This research endeavors to elucidate the integration of bio-inspired optimization techniques that improve disease diagnostics through deep learning models. …”
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Short-Term Power Load Prediction Based on Level Processing Method and Improved GWO Algorithm by Yuntong Li

“…To address this issue, this study introduces level processing method and improved grey wolf genetic algorithm to predict short-term power load and optimize the power load prediction accuracy. …”
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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment by C. Labesh Kumar, Suresh Betam, Denis Pustokhin, E. Laxmi Lydia, Kanchan Bala, Rajanikanth Aluvalu, Bhawani Sankar Panigrahi

“…For the DDoS attack classification process, the attention mechanism with convolutional neural network and bidirectional gated recurrent units (CNN-BiGRU-AM) is employed. To ensure optimal performance of the CNN-BiGRU-AM model, hyperparameter tuning is performed by utilizing the seagull optimization algorithm (SOA) model to enhance the efficiency and robustness of the detection system. …”
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Research on SBMPC Algorithm for Path Planning of Rescue and Detection Robot by Lin-Lin Wang, Li-Xin Pan

“…Sampling-Based Model Predictive Control (SBMPC) algorithm is proposed basing on the construction of cost function and predictive kinematics model. …”
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Improved Splitting-Integrating Methods for Image Geometric Transformations: Error Analysis and Applications by Hung-Tsai Huang, Zi-Cai Li, Yimin Wei, Ching Yee Suen

“…The splitting-integrating method (SIM) is well suited to the inverse transformation <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>T</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> of digital images and patterns, but it encounters difficulties in nonlinear solutions for the forward transformation <i>T</i>. We propose improved techniques that entirely bypass nonlinear solutions for <i>T</i>, simplify numerical algorithms and reduce computational costs. …”
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Network Security Situational Awareness Based on Improved Particle Swarm Algorithm and Bidirectional Long Short-Term Memory Modeling by Peng Zheng, Yun Cheng, Wei Zhu, Bo Liu, Shuhong Liu, Shijie Wang, Jinyin Bai

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Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO by Enlai ZHANG, Yi CHEN, Liang SU, Ruoyu ZHONGLIAN, Xianyi CHEN, Shangfeng JIANG

“…Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost, and PSO-XGBoost through data training. …”
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Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks by Haiping LIANG, Haoyan SHI, Yan WANG, Yingpei LIU, Xinming WANG

“…An improved particle swarm optimization (PSO) algorithm is proposed for the optimization model, which uses such methods as the multi-dimensional indefinite length coding, sub-group collaborative optimization, and Monte-Carlo-simulation-based fitness evaluation to improve the standard PSO algorithm. …”
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Enhancing Route Optimization in Road Transport Systems Through Machine Learning: A Case Study of the Dakhla-Paris Corridor by Najib El Karkouri, Lahcen Hassine, Younes Ledmaoui, Hasna Chaibi, Rachid Saadane, Nourddine Enneya, Mohamed El Aroussi

“…However, these systems face numerous challenges, particularly regarding safety, cost, and route optimization, requiring innovative and practical solutions to improve their overall performance. …”
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Grey modeling method for approximate exponential sequence of optimizing initial condition by Yun YUE, Guangyue LU

“…Grey GM（1,1）prediction method is only suitable for the prediction model of the original sequence which satisfies the characteristic of the approximate exponential through the accumulated generating operation.In order to widen the application range of the traditional grey prediction model,a new method,dubbed DGM（1,1,c,β）model（direct grey model）,was proposed to improve the accuracy of grey GM（1,1）prediction by optimizing initial conditions.DGM（1,1,c,β）model was established for the original sequence conforming to the approximate exponential and the model parameters were obtained by the particle swarm optimization algorithm.Both the simulation and analysis of the example demonstrate that the proposed method is more effective and practical.…”
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Application of improved ant-lion algorithm for power systems. by Wenjing Wang, Renjun Zhou

“…The required number of iterations was significantly better than other algorithms. In the verification of solving economic load dispatch, the improved ant-lion optimizer achieved a total fuel cost reduction of 0.10% -2.39% and 6% in both 3-unit and 6-unit simulations, respectively, compared to the other three algorithms. …”
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Parameter Sensitivity Analysis and Irrigation Regime Optimization for Jujube Trees in Arid Regions Using the WOFOST Model by Shihao Sun, Yingjie Ma, Pengrui Ai, Ming Hong, Zhenghu Ma

“…The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ET_c combined with 300 kg/ha of potassium application. …”
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The Impact of Different Parallel Strategies on the Performance of Kriging-Based Efficient Global Optimization Algorithms by Hang Fu, Qingyu Wang, Takuji Nakashima, Rahul Bale, Makoto Tsubokura

“…A parallel efficient global optimization (EGO) algorithm with a pseudo expected improvement (PEI) multi-point sampling criterion, proposed in recent years, is developed to adapt the capabilities of modern parallel computing power. …”
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