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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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

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Optimization design of urban rainwater and flood drainage system based on SWMM by Cao Xihan

“…Traditional drainage system design methods are no longer able to meet the complex needs of modern cities. To improve the overall performance of urban rainwater and flood drainage system, a coupling model of multiobjective optimization algorithm and rainstorm management model is constructed. …”
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An Optimization Model for Shell Plate Seam Landing Using Minimum Manufacturing Cost and a Solution by Genetic Algorithm by Ho Rim Pae, Min Hyok Jon, Chol Jun Pak

“…In this paper, a new optimization model and its solution method for the landing of seams and butts (for convenience, seam and butt are simply called seam) on the ship hull surface are proposed in order to improve the shipbuilding efficiency. …”
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A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements by Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy

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An Improved Maximum Power Point Tracking Control Scheme for Photovoltaic Systems: Integrating Sparrow Search Algorithm-Optimized Support Vector Regression and Optimal Regulation fo... by Mingjun He, Ke Zhou, Yutao Xu, Jinsong Yu, Yangquan Qu, Xiankui Wen

“…The sparrow search algorithm (SSA), recognized for its excellent global search capability, was employed to optimize the hyperparameters of SVR to further increase the prediction accuracy. …”
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Aerodynamic Optimization Method for Propeller Airfoil Based on DBO-BP and NSWOA by Changjing Guo, Zhiling Xu, Xiaoyan Yang, Hao Li

“…To address the issues of tedious optimization processes, insufficient fitting accuracy of surrogate models, and low optimization efficiency in drone propeller airfoil design, this paper proposes an aerodynamic optimization method for propeller airfoils based on DBO-BP (Dum Beetle Optimizer-Back-Propagation) and NSWOA (Non-Dominated Sorting Whale Optimization Algorithm). …”
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Improved artificial bee colony algorithm for large scale colored bottleneck traveling salesman problem by Wenyong DONG, Xueshi DONG, Yufeng WANG

“…An improved artificial bee colony algorithm (IABC) was applied to solve the large scale CBTSP. …”
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Optimizing Vehicle Body Cross-Sections Using a Parametric Mathematical Model by Zhaohui Hu, Shuai Mo, Huang Liu, Fuhao Mo

“…In the example of this paper, after the optimization of the established meshless model, the mass of the whole vehicle is reduced by 30%, and the stiffness of the whole vehicle is greater than that of the benchmark vehicle (5128 N/mm, 4386 N/mm), and at the same time, compared with the conceptual design method of the body of CAE technology, the modeling time is greatly reduced, and the computational efficiency of the analytical method is greatly improved compared with the finite element method.…”
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Dynamic Modeling and Parameter Optimization of Potato Harvester Under Multi-Source Excitation by Jianguo Meng, Zhipeng Li, Zheng Li, Yanzhou Li, Wenxia Xie

“…On this basis, the Bayesian optimization algorithm was used to obtain optimal connection parameters. …”
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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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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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Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling by Raman Kumar, Jasgurpreet Singh Chohan, Sandeep Singh, Shubham Sharma, Yadvinder Singh, S. Rajkumar

“…Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. The advanced optimization and production techniques have yielded promising results which have been validated by advanced algorithms. …”
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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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A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm by G. Ramkumar, Satyajeet Sahoo, T. M. Amirthalakshmi, S. Ramesh, R. Thandaiah Prabu, Kasipandian Kasirajan, Antony V. Samrot, A. Ranjith

“…This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. …”
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Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms by Sawsan Akram Hassan, Mahir M. Hason, Ammar N. Hanoon, Ali A. Abdulhameed

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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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Multi-objective coordinated control and optimization for photovoltaic microgrid scheduling by Da Yu, Kai Hou, Xu Lin, Guoyang Cai, Xin Shan, Weihua Wang

“…This paper proposes a multi-objective coordinated control and optimization system for PV microgrids. To address the challenges of slow convergence and local optima in traditional PV microgrid scheduling methods, this study introduced an improved multiple objective particle swarm optimization (IMOPSO) algorithm that integrates an adaptive inertia weight adjustment strategy based on optimal similarity and a multi-directional iterative Pareto solution archive update mechanism. …”
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LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction by Dana R. Hamad, Tarik A. Rashid

“…The current study introduces an optimization algorithm, Learner Performance-Based Behavior with Simulated Annealing (LPBSA), integrated with Multilayer Perceptron (MLP) as a neural network technique to improve disease prediction accuracy. …”
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A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method by Nerjes Rahemi, Kurosh Zarrinnegar, Mohammad Reza Mosavi

“…In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. …”
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