From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy by Rafael Herschberg, Lisa Rateau, Laure Martinelli, Fanny Balbaud-Célérier, Jean Dhers, Anna Fraczkiewicz, Gérard Ramstein, Franck Tancret

“…In this case, the method is applied to the design of wear-resistant hard-facing alloys by also associating it with a combinatorial optimization of their composition by a multi-objective genetic algorithm. …”
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A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit by Xiaoyu Yue, Lijun Fu, Siyang Liao, Jian Xu, Deping Ke, Huiji Wang, Shuaishuai Feng, Jiaquan Yang, Xuehao He

“…This paper combines the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), whale optimization algorithm (WOA), and long short-term memory network (LSTM) to propose a CEEMDAN-WOA-LSTM prediction model for electricity price scenarios. …”
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Training Large Models on Heterogeneous and Geo-Distributed Resource with Constricted Networks by Zan Zong, Minkun Guo, Mingshu Zhai, Yinan Tang, Jianjiang Li, Jidong Zhai

“…To achieve this goal, we formulate the model partitioning problem among heterogeneous hardware and introduce a hierarchical searching algorithm to solve the optimization problem. …”
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Application of KTA-KELM in Fault Diagnosis of Rolling Bearing by Zhuo Wang, Wenjun Zhao, Tao Ma, Zhijun Li, Bo Qin

“…Then，the Kernel Target Alignment（KTA） parameters of maximum KTA value Ai and the kernel parameter σi are initialized, and the different kernel parameter values are adjusted by judging the distance between the kernel matrix and the ideal target matrix，so as to obtain the minimum corresponding maximum kernel arrangement value when the kernel matrix distance is obtained，and the kernel parameter at this time is optimal. Finally，the high-dimensional feature vector set of the above rolling bearing is used as input to learn the KTA-KELM algorithm, the state recognition model of rolling bearing is built based on KTA-KELM algorithm. …”
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A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model by Yi Lu, Jie Yin, Peiyan Chen, Hui Yu, Sirong Huang

“…Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. …”
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A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells by Bhukya Laxman, Ramesh Gugulothu, Surender Reddy Salkuti

“…Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. …”
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A Generalized Dynamic Potential Energy Model for Multiagent Path Planning by Liu He, Haoning Xi, Tangyi Guo, Kun Tang

“…In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. …”
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Economic Dispatch of Fully Renewable Energy System Considering Uncertainty of both Energy Source and Load by Fan YU, Honghai NIU, Bing LI, Yang ZHAO, Pei CHEN, Xiaochen GUAN, Yu YANG

“…Considering the uncertainty of both energy source and load, a day-ahead economic dispatch model based on two-stage robust optimization was built and solved by column constraint generation (C & CG) algorithm. …”
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Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS by Areti Kotsi, Ioannis Politis, Evangelos Mitsakis

“…The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. …”
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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam by Yeong-Nam Jeon, Jae-ha Ko

“…The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. …”
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Enhancing resilience of distribution system under extreme weather: Two-stage energy storage system configuration strategy based on robust optimization by Ye He, Hongyun Fu, Andrew Y. Wu, Hongbin Wu, Ming Ding

“…Finally, a two-stage robust optimization model for the ESS is established to improve DN resilience with the objective of minimizing the comprehensive economic cost of the ESS and the annual comprehensive weighted load loss, which is solved using the column-and-constraint generation algorithm (C&CG). …”
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Combining miRNA concentrations and optimized machine-learning techniques: An effort for the tomato storage quality assessment in the agriculture 4.0 framework by Seyed Mohammad Samadi, Keyvan Asefpour Vakilian, Seyed Mohamad Javidan

“…However, the RF, with hyperparameters optimized by the genetic algorithm, was able to improve the R2 values of the prediction of storage temperature and period to 0.96 and 0.89. …”
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Study on Outdoor Spectral Inversion of Winter Jujube Based on BPDF Models by Yabei Di, Jinlong Yu, Huaping Luo, Huaiyu Liu, Lei Kang, Yuesen Tong

“…In the future, it is necessary to further optimize the dynamic adjustment mechanism of the model parameters and improve the ability of environmental interference correction by combining multi-source data fusion.…”
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Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network by Yifei Zhang, Gengxin Zhang, Dawei Wu, Qian Wang, Ebrahim Nadimi, Penghua Shi, Hongming Xu

“…The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. …”
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RETRACTED ARTICLE: Effects of Curcumin-coated Nanomicelles on Cerebral Ischemia Injury in Rats by Regulating lncRNA GAS5 and NF-κB Signaling Pathways by Zhiyue Zhang, Lei Zhang, Xuan Cai, Xing Li, Cheng Wei, Gangli Yan, Fengguang Li

“…These results indicate that the computer intelligent algorithm can be used to evaluate the behaviour of rats, EEG can reflect the changes of cerebral cortical function, and Cur-PLG-mPEG-PCL can improve neurological function and degree of brain injury.…”
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Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning by Xue Feng, Chi Xu, Xi Jin, Changqing Xia, Jing Jiang

“…To address the end-edge computation offloading challenge in the multi-terminal and multi-server environment, this paper proposes an intelligent computation offloading algorithm based on Lyapunov optimization and deep reinforcement learning. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The models were applied to be constructed in R-project (version 3.5.2) and the ‘caret’ package was applied to tune the machine learning algorithm parameters. …”
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A location semantic privacy protection model based on spatial influence by Linghong Kuang, Wenlong Shi, Xueqi Chen, Jing Zhang, Huaxiong Liao

“…Experimental results demonstrate that the proposed model improves traffic trajectory data availability and semantic protection performance by 14% and 46.5%, respectively, compared to traditional models.…”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. …”
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Optimized physics-informed neural networks for deciphering of external source pollutants in a swirling flow induced by a constant torsional motion by Shridhar M, Umair Khan, Rahul Makwana, Ankur Kulshreshta, N.B. Naduvinamani

“…The flow, heat and mass transport attributes are assessed using the Physics-informed neural network (PINN). This model is optimized by a hybrid genetic algorithm and particle swarm optimization to address the flow, heat and mass transport attributes via neural networks. …”
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