Spatiotemporal-Dependent Vehicle Routing Problem Considering Carbon Emissions by Ziqi Liu, Yeping Chen, Jian Li, Dongqing Zhang

“…A hybrid adaptive genetic algorithm with elite neighborhood search is developed to solve the problem. …”
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Research on collaborative scheduling strategies of multi-agent agricultural machinery groups by Ziyi Wang, Fan Zhang, Shiji Ma, Hailong Wang, Shunyao Zhang, Xiaozhong Gao

“…It introduces a Multi-Center and Multi-Machine Path Planning Algorithm Based on Deep Reinforcement Learning (MCMPP-DRL). …”
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Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis by Javad Shirani Shamsabadi, Saeid Ansari Mahyari, Mostafa Ghaderi-Zefrehei

“…The aim of this study was to compare the performance of three adaptive neuro-fuzzy inference systems (ANFIS) classification methodologies in classifying mastitis in Holstein dairy cattle: gradient descent (GD)-based ANFIS (GD-ANIFIS), particle swarm optimization (PSO)-based ANFIS (PSO-ANFIS) and genetic algorithm (GA)-based ANFIS (GA-ANFIS). …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators by Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen

“…Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. …”
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Joint Optimization of Wireless Charging Station Location and Operation Scheduling for Electric Buses Under Uncertain Demand by Jiacheng Li, Masato Noto, Yang Zhang, Jia Guo

“…An adaptive genetic algorithm (AGA) is developed to solve the model efficiently. …”
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Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers. by Zhipeng Huang, Limin Yang, Jinlian Li, Tao Zhang, Zixian Qu, Yusen Miao

“…Using the Lanzhou-Xi'an high-speed railway corridor as a case study, we apply a genetic algorithm combined with a nested Frank-Wolfe method to solve the model. …”
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Tire-road surface characteristics estimation for skid-steered wheeled vehicle by Ao Li, Xiaolin Guo, Yuzheng Zhu, Xueyuan Li, Xin Gao

“…To address these issues, we propose a hybrid off-line and on-line estimation approach. Initially, a dynamic model for multi-axle vehicles and a brush-based tire model were constructed. …”
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Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting by Vijayalakshmi S, John A, Sunder R, Senthilkumar Mohan, Sweta Bhattacharya, Rajesh Kaluri, Guang Feng, Usman Tariq

“…The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.…”
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Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments by Amin Taheri-Garavand, Mojgan Beiranvandi, Abdolreza Ahmadi, Nikolaos Nikoloudakis

“…The current research was carried out to develop a genetic algorithm-based artificial neural network (ΑΝΝ) model able of simulating the levels of antioxidants in savory when using soil amendments [biochar (BC) and superabsorbent (SA)] under drought. …”
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Collision Probability Evaluation of LAMOST Robotic Fiber Positioners in the Design Phase by Baolong Chen, Jianping Wang, Jiahao Zhou, Zhigang Liu, Hongzhuan Hu, Zengxiang Zhou, Feifan Zhang

“…During collision probability calculation, we consider factors such as RFP structure, target allocation, motion requirements, and mechanical errors into consideration. Based on this, we employ the genetic algorithm to optimize RFP arrangements with the lowest collision probability and show its function in the design phase. …”
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Enhancing prediction of wildfire occurrence and behavior in Alaska using spatio-temporal clustering and ensemble machine learning by A. Ahajjam, M. Allgaier, R. Chance, E. Chukwuemeka, J. Putkonen, T. Pasch

“…This ensemble model’s performance is benchmarked across four prediction horizons (same-day, +7 days, +30 days, +90 days) and against various conventional ML and deep learning techniques. …”
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Integrated optimization and coordination of cascaded reservoir operations: Balancing flood control, sediment transport and ecosystem service by Xinmiao Cao, Teng Lin, Jiahui Li, Ting Zhou

“…To address the optimization model, an elite mutation‐based multi‐objective particle swarm optimization (MOPSO) algorithm that integrates genetic algorithms (GA) is developed. …”
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Optimal design and performance prediction of stepped honeycomb labyrinth seal using CFD and ANN by Geunseo Park, Min Seok Hur, Tong Seop Kim

“…In the first stage, incremental Latin hypercube sampling (i-LHS) was used to select geometric design points for training the ANN with CFD providing the leakage performance data. An ANN-based performance prediction metamodel was developed, and a genetic algorithm was applied to the metamodel to optimize seal geometry, achieving a 12.34% improvement in leakage performance over the reference seal. …”
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Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection by Jessica Fernandes Lopes, Sylvio Barbon Junior, Leonimer Flávio de Melo

“…This work introduces a meta-modeling scheme designed to automate the recommendation of hyperparameters for the CUSUM algorithm. …”
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A method for the identification of lactate metabolism-related prognostic biomarkers and its validations in non-small cell lung cancer by Weiyang Yang, Miao Gu, Yabin Zhang, Yunfan Zhang, Tao Liu, Di Wu, Juntao Deng, Min Liu, Youwei Zhang

“…The existing methods for the construction of prognosis prediction models are mostly based on single models such as linear models, SVM, and decision trees. …”
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Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights. by Jincan Li, Shengyue Wang

“…A random survival forest model was developed integrating molecular features with LSC17 scores, validated across all cohorts. …”
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Electromagnetic Analysis and Multi-Objective Design Optimization of a WFSM with Hybrid GOES-NOES Core by Kyeong-Tae Yu, Hwi-Rang Ban, Seong-Won Kim, Jun-Beom Park, Jang-Young Choi, Kyung-Hun Shin

“…Finite element analysis (FEA) was employed to compare electromagnetic performance across the configurations. Subsequently, a multi-objective optimization was conducted using Latin Hypercube Sampling, meta-modeling, and a genetic algorithm to maximize power density and efficiency while minimizing torque ripple. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…We compared methods including the kitchen sink model, Bross-based hdPS, Hybrid hdPS, LASSO, Elastic Net, Random Forest, XGBoost, and Genetic Algorithm (GA). …”
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Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning by Yusuke Kobayashi, Naoki Fujiwara, Yuki Murakami, Shoichi Ishida, Sho Kinguchi, Tatsuya Haze, Kengo Azushima, Akira Fujiwara, Hiromichi Wakui, Masayoshi Sakakura, Kei Terayama, Nobuhito Hirawa, Tetsuo Isozaki, Hiroaki Yasuzaki, Hajime Takase, Yuichiro Yano, Kouichi Tamura

“…Machine learning models based on blood and salivary biomarkers achieved an F1-score of approximately 0.7 in predicting fatigue dimensions. …”
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