Analysis and Dynamic Prediction of Bus Dwell Time Under Rainfall Conditions by Baoyun SUN, Yaping YANG, Lei DONG, Honglin LU, Zimin WANG

“…Support vector machine, k-nearest neighbour and backpropagation (BP) prediction models were established, and the BP neural network model, having the best prediction effect, was optimised using a genetic algorithm (GA). The constructed GA-BP prediction model was more realistic than the BP prediction model and can be used to predict bus dwell times under rainfall conditions. …”
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Integration research of blockchain and social networks in rural management systems under fuzzy cognitive environment by Wencun Wang, Jun Yao, Di Zhao, Can Huang

“…The system automatically invokes the Matlab dynamic link library and employs a hybrid genetic algorithm to plan delivery routes. This study presents key findings on implementing an intelligent rural management system incorporating fuzzy sets, blockchain, and IoT technology. …”
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Multi-Item Multiperiodic Inventory Control Problem with Variable Demand and Discounts: A Particle Swarm Optimization Algorithm by Seyed Mohsen Mousavi, S. T. A. Niaki, Ardeshir Bahreininejad, Siti Nurmaya Musa

“…To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.…”
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A Multifactory Integrated Production and Distribution Scheduling Problem with Parallel Machines and Immediate Shipments Solved by Improved Whale Optimization Algorithm by Vahid Abdollahzadeh, Isa Nakhaikamalabadi, Seyyed Mohammad Hajimolana, Seyyed Hesamoddin Zegordi

“…Moreover, it confirms the capability of the improved whale optimization algorithm (IWOA) to solve the medium-scale instances; however, the results indicate the better performance of genetic algorithm (GA) for the large-scale instances.…”
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Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation by Jae-Hyun Seo, Yong Hee Lee, Yong-Hyuk Kim

“…In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. …”
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Smart Cylindrical Dome Antenna Based on Active Frequency Selective Surface by Tongyu Ding, Shaoqing Zhang, Liang Zhang, Yanhui Liu

“…Moreover, in order to overcome the unavailable analytical synthesis caused by complex mutual coupling between columns, we develop a genetic algorithm based optimization system and conducted a serial of experiments to evaluate the high-gain, nulling, continuously steering, and frequency-invariant ability. …”
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GA-ARMA Model for Predicting IGS RTS Corrections by Mingyu Kim, Jeongrae Kim

“…We propose applying a genetic algorithm autoregressive moving average (GA-ARMA) model to predict the IGS RTS corrections during data loss periods. …”
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A Modified Asymmetric Bouc–Wen Model-Based Decoupling Control of an XY Piezoactuated Compliant Platform with Coupled Hysteresis Characteristics by Xubin Zhou, Weidong Chen, Qing Xiao, Xiangsen Kong, Xingtian Liu, Liping Zhou, Yichong Dong, Quan Zhang

“…To establish the hysteresis characteristics of the piezo-driven manipulator, a modified Bouc–Wen model has been proposed, and a Genetic Algorithm-based Particle Swarm Optimization (GA-PSO) was adopted to recognize the parameters of the model. …”
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Defining the Optimal Level of Pressure Fluctuations at Pressure Control Management Strategy for Reducing Water Losses in Water Networks by Talha İbrahim Arduçoğlu, Furkan Boztaş, Mahmut Fırat

“…In the next stage, the operating scenario for the flow-sensitive advanced pressure management was started to be created. Genetic algorithm was used in the MATLAB environment to create the most suitable scenario. …”
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Curing simulation and data-driven curing curve prediction of thermoset composites by Chenchen Wu, Ruming Zhang, Pengyuan Zhao, Liang Li, Dingguo Zhang

“…Then, the temperature–time and the resulting degree-of-cure-time curves obtained from finite element simulations were created for training the prediction models using machine learning approaches of support vector regression (SVR), back propagation (BP) neural network and BP neural network optimized by genetic algorithm (GA-BP). The validation and evaluation indices illustrate that the degree-of-cure curve prediction model trained by the GA-BP neural network yields the highest accuracy.…”
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Optimal compensation method for centrifugal impeller considering aerodynamic performance and dimensional accuracy by Tao Zhou, Sitong Xiang, Hainan Zhang, Jianguo Yang

“…Finally, based on the mapping model, the second-generation non-dominated sorting genetic algorithm was used to optimize the control points of the mirror compensation surface, and thereby obtain the optimal compensation surface. …”
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Optimized Skip-Stop Metro Line Operation Using Smart Card Data by Peitong Zhang, Zhanbo Sun, Xiaobo Liu

“…Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. …”
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Stock Price Change Rate Prediction by Utilizing Social Network Activities by Shangkun Deng, Takashi Mitsubuchi, Akito Sakurai

“…In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. …”
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Risk-Based Multiobjective Optimal Seismic Design for RC Piers Using the Response Surface Method and NSGA-II by Sicong Hu, Yixuan Zou, Yufeng Gai, Zheng Huang, Guquan Song

“…The Pareto optimal solutions of piers are determined by applying an improved version of the nondominated sorting genetic algorithm (NSGA-II). As a case study, the proposed optimal seismic design method is applied to a continuous concrete box girder bridge. …”
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Optimizing N-1 Contingency Rankings Using a Nature-Inspired Modified Sine Cosine Algorithm by Irnanda Priyadi, Novalio Daratha, Teddy Surya Gunawan, Kalamullah Ramli, Febrian Jalistio, Hazlie Mokhlis

“…Compared with established methods such as Ant Colony Optimization (ACO) and Genetic Algorithm (GA), MSCA exhibits superior computational efficiency while maintaining competitive accuracy. …”
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Study on Applicability of Xin'anjiang Model and Tank Model in Flood Forecasting in Majiagou Reservoir by MA Jinghang, XIAN Yongcai, HE Xueping, LIU Ming, HAN Muyuan, DU Bailin, RUAN Bingnan, XU Liujia, WU Lei

“…Flood forecasting is one of the important non-engineering flood control measures and is the main basis for flood control command and decision-making.In order to avoid the uncertainty of the prediction results of a single model,the Majiagou Reservoir in Chenggu County was taken as the object to simulate the daily runoff and flood process from 2019 to 2021 by using the Xin'anjiang model and tank model respectively,and the simulation results and accuracy of the two models were compared by using the model parameters calibrated and optimized by the genetic algorithm.In the daily runoff simulation,the simulation effect of the tank model is better than that of the Xin'anjiang model,with a relative error of flood volume of less than 16%,a relative error of flood peak of less than 4%,a difference of peak time of less than 1 h,and a Nash-Sutcliffe efficiency coefficient of greater than 0.58,all of which meet the evaluation accuracy requirements of the Standard for Hydrological Information and Hydrological Forecasting,and the simulation effect of deluge in the reservoir is ideal.In the flood process simulation,the difference of peak time between the two models is similar;the simulation effect of the Xin'anjiang model is smoother,and the flood volume and flood peak simulated by the tank model are closer to the measured flow process.On the whole,the tank model is more suitable for flood forecasting in Majiagou Reservoir than the Xin'anjiang model.…”
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Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling by Mohsen Abbod, Ahmad Mohammad

“…The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. …”
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Optimization of Driving Axle Housing of Dump Truck based on Robustness Selection by Ronghui Lin, Peng Zhou

“…Three groups of 9 optimization sizes are obtained Through the deterministic optimization by multi-objective Genetic Algorithm. The L⁹ (3⁴) orthogonal table is constructed，the robust selection of deterministic optimal size is carried out，the optimal size of good robust performance is obtained based on signal-to-noise ratio <italic>η</italic>. …”
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Optimization Design of Diaphragm Profile based on Kriging Model by Geng Hu, Zhigang Chen, Ding Zhang

“…Finally，the initial Kriging model is updated with MP （minimizing the predicted objective function） infill-sampling criterion and genetic algorithm，and the optimal design is obtained via the final Kriging model. …”
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Experimental and Mathematical Studies for Optimality of GTAW Parameters on Similar and Dissimilar Steel Substrates by Bandaru Kiran, Dega Nagaraju, Senthil Kumaran Selvaraj, Baye Molla

“…Furthermore, tensile tests are carried out for the weldments with optimal predefined weld parameters, and subsequently, the welded specimens with better tensile strength are discussed in the results. Genetic algorithm and mathematical modelling are opted for selecting the final optimal welding parameters. …”
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