UAV Path Planners in Complex Environments: A Multi-Dimensional Perturbation Based on Artificial Bee Colony by Xuzhao Chai, Guanhao Zhou, Haoyu Wang, Ping Liu, Zhongyun Liu, Chao Li, Li Yan

“…In the onlooker bee phase, the curvature-guided elite neighbor search strategy is used to prioritizes high-curvature waypoints, enhancing optimization efficiency in complex terrain. Furthermore, path costs are independently modeled in the horizontal and vertical directions. …”
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Optimization of thermal conductivity in coir fibre-reinforced PVC composites using advanced computational techniques by Saksham Anand, Venkatachalam Gopalan, Shenbaga Velu Pitchumani

“…To address these challenges, the study uses Response Surface Methodology (RSM) and three nature-inspired optimization methods viz. Particle Swarm Optimization (PSO), Dragonfly Optimization (DFO) and Cuckoo Search Algorithm (CSA) to improve factors like fibre content, particle size and chemical treatment. …”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…Combined with particle swarm optimization (PSO), the hyper-parameter combination optimization of support vector machine, decision tree and random forest models is carried out to obtain the optimal parameter combination of the model and improve the prediction performance and generalization ability of the model. …”
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Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network by Kefei Cheng, Xiaoyong Guo, Xiaotong Cui, Fengchi Shan

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Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network by Zhanjun Hao, Nanjiang Qu, Xiaochao Dang, Jiaojiao Hou

“…In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. …”
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Influence of soil parameters on dynamic compaction: numerical analysis and predictive modeling using GA-optimized BP neural networks by Yu Zhang, Xueshui Chen, Huakang Ge, Zhigang Guo, Xu Li

“…Orthogonal experimental design and single factor analysis were used to quantify the influence of each parameter on the compaction volume. In order to improve the prediction accuracy, this paper introduces genetic algorithm (GA) to optimize the BP neural network model, constructs a multi-factor dynamic compaction prediction model, and compares it with the traditional BP model. …”
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Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network by Yilong Pan, Yaxin Yu, Junwei Zhou, Wenbing Qin, Qiang Wang, Yinghao Wang

“…The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. …”
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Recurrent academic path recommendation model for engineering students using MBTI indicators and optimization enabled recurrent neural network by Anupama V, Sudheep Elayidom M

“…To address this issue, an intelligent recommendation model is proposed that assists students in discovering the most suitable academic path based on their personal background and personality traits. …”
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Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model by Gonglin Xu, Mei Zhang, Wanli Chen, Zhihui Wang

“…The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. …”
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Model-Data Hybrid-Driven Real-Time Optimal Power Flow: A Physics-Informed Reinforcement Learning Approach by Ximing Zhang, Xiyuan Ma, Yun Yu, Duotong Yang, Zhida Lin, Changcheng Zhou, Huan Xu, Zhuohuan Li

“…Specifically, the proposed methodology uses the proximal policy optimization (PPO) algorithm as the agent’s foundational framework and constructs a PI-actor network embedded with prior model knowledge derived from power flow sensitivity into the agent’s actor network via the PINN method, which achieves dual optimization objectives: (a) enhanced environmental perceptibility to improve experience utilization efficiency via gradient-awareness from model knowledge during actor network updates, and (b) improved user trustworthiness through mathematically constrained action gradient information derived from explicit model knowledge, ensuring actor updates adhere to safety boundaries. …”
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Transformer based models with hierarchical graph representations for enhanced climate forecasting by T. Bhargava Ramu, Raviteja Kocherla, G. N. V. G. Sirisha, V. Lakshmi Chetana, P. Vidya Sagar, R. Balamurali, Nanditha Boddu

“…To improve computational efficiency and feature selection, we introduce a hybrid optimization approach (HWOA-TTA) that combines the Whale Optimization Algorithm (WOA) and Tiki-Taka Algorithm (TTA). …”
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Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment by A. Koraniany, G.R. Ansarifar

“…By integrating this neural network with a genetic algorithm, optimization was carried out to identify the optimal fuel placement and enrichment for loading the UTVS fuel assembly into the targeted reactor core. …”
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Numerical Study on Energy-saving Optimal Control of Rotary Desiccant Wheel System by Zhang Nan, Yao ye, Jia Dongxin

“…A weight factor was introduced to combine the energy consumption and dehumidification performance of the system as the optimization objective. An energy-saving optimization control model of the desiccant wheel system was proposed with an improved PSO as the solution algorithm. …”
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AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm by Lin Yang

“…These models classify buildings based on energy consumption patterns, predicting energy needs and identifying areas for improvement. …”
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Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient by Leran Chen, Ping Ji, Yongsheng Ma

“…A general-purpose machine learning algorithm depends on a large amount of data and requires abundant computing power support, relies on the average level to describe the model performance, and cannot achieve optimal results on a specific problem. …”
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Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique by Karim Gasmi, Najib Ben Aoun, Najib Ben Aoun, Khalaf Alsalem, Ibtihel Ben Ltaifa, Ibrahim Alrashdi, Lassaad Ben Ammar, Manel Mrabet, Abdulaziz Shehab

“…This study underscores the potential of combining advanced deep learning models with a genetic algorithm-optimized weighting strategy to tackle complex medical classification tasks. …”
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Economic approach for optimal allocation of irrigation water in water-scarce region by Xueliang Zhang, Li Ren, Jianshi Zhao

“…This study proposes an economic approach to spatial irrigation allocation, based on the marginal benefit criterion, to improve both the explainability and efficiency of the optimization process. …”
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Research on Comprehensive Control Strategy of Loop Closing Currentin 10 kV Distribution Network by LIU Song, HUANG Chun

“…An improved harmony algorithm based on dynamic parameters and Pareto optimal is used to solve the model, then a recursive algorithm is used to solve the action sequence of control elements considering process security. …”
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Mechanism- and data-driven algorithms of electrical energy consumption accounting and prediction for medium and heavy plate rolling by Qiang Guo, Zimeng Zhou, Jie Li, Fengwei Jing

“…Energy consumption accounting and prediction in the medium and thick plate rolling process are crucial for controlling costs, improving production efficiency, optimizing equipment management, and enhancing the market competitiveness of enterprises. …”
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Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. …”
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