Three Strategies Enhance the Bionic Coati Optimization Algorithm for Global Optimization and Feature Selection Problems by Qingzheng Cao, Shuqi Yuan, Yi Fang

“…To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. …”
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Advancing Dynamic Emergency Route Optimization with a Composite Network Deep Reinforcement Learning Model by Jin Zhang, Hao Xu, Ding Liu, Qi Yu

“…This method utilizes the actor–critic framework, combined with attention mechanisms, pointer networks, and long short-term memory neural networks, to determine effective disaster relief path, and it compares the obtained scheduling scheme with the results obtained from the DRL algorithm based on the single-network model and ant colony optimization (ACO) algorithm. …”
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Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…Based on a three-stage experiment, we first find that the random forest classifier has the best performance in predicting corporate fraud among 17 machine learning models. We then implement the sliding time window approach to handle population drift, and the optimal training window found demonstrates the existence of population drift in fraud detection and the need to address it for improved model performance. …”
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Enhanced prediction of corrosion rates of pipeline steels using simulated annealing-optimized ANFIS models by Ali Hussein Khalaf, Bing Lin, Ahmed N. Abdalla, Zhongzhi Han, Ying Xiao, Junlei Tang

“…The SA-ANFIS model offers a robust, optimized tool for predicting corrosion in petroleum pipelines, significantly improving prediction accuracy under harsh conditions.…”
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An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…With this inspiration, this study presents a new snake optimization algorithm with a DL-assisted GC classification (SOADL-GCC) approach. …”
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Optimizing adaptive modulation technique using standard propagation model for enhanced wireless communication channels by Rahim Khan, Zahid Ullah Khan, Sher Taj, Sajid Ullah Khan, Javed Khan, Nazik Alturki, Sultan Alanazi

“…To ensure optimal configuration, an advanced optimization algorithm is employed to dynamically select the most effective SPM parameters, enabling robust performance across varying channel conditions. …”
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Distributed robust planning for new power system considering uncertainty and frequency security by Xinxin Zheng, Dahai Zhang, Zhaohong Bie, Xue Wu

“…Firstly, establish a system frequency model that provides frequency support for a fast frequency response energy storage system, and propose an improved segmented linearization algorithm to transform the constructed frequency safety constraints. …”
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Daily runoff forecasting using novel optimized machine learning methods by Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band

“…This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. …”
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Optimization of Offshore Saline Aquifer CO₂ Storage in Smeaheia Using Surrogate Reservoir Models by Behzad Amiri, Ashkan Jahanbani Ghahfarokhi, Vera Rocca, Cuthbert Shang Wui Ng

“…This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. …”
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Multijunction solar cell parameter estimation based on metaheuristic algorithms by Marwa M. Elzalabani, Doaa M. Atia, Aref Y. Eliwa, Belal A. Abou Zalam, Mahmoud S. AbouOmar

“…Several mathematical models exist for MJSC parameter estimation— Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM)—each offering a trade-off between complexity and accuracy, improved efficiency, and reliable operation. …”
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An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis by Hao Yan, Liangliang Shang, Wan Chen, Mengyao Jiang, Tianqi lu, Fei Li

“…The AO algorithm is further enhanced by incorporating chaos mapping, implementing a refined balanced search strategy, and fine-tuning parameter $$G_2$$ , which collectively improve its ability to escape local optima and conduct global searches, thus strengthening the robustness of the model during parameter optimization. …”
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SMA-YOLO: An Improved YOLOv8 Algorithm Based on Parameter-Free Attention Mechanism and Multi-Scale Feature Fusion for Small Object Detection in UAV Images by Shenming Qu, Chaoxu Dang, Wangyou Chen, Yanhong Liu

“…Using YOLOv8n as the baseline, SMA-YOLO is evaluated on the VisDrone2019 dataset, achieving a 7.4% improvement in mAP@0.5 and a 13.3% reduction in model parameters, and we also verified its generalization ability on VAUDT and RSOD datasets, which demonstrates the effectiveness of our approach.…”
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Cooperative Sleep and Energy-Sharing Strategy for a Heterogeneous 5G Base Station Microgrid System Integrated with Deep Learning and an Improved MOEA/D Algorithm by Ming Yan, Tuanfa Qin, Wenhao Guo, Yongle Hu

“…This paper proposes a cooperative sleep and energy-sharing strategy for heterogeneous 5G base station microgrid (BSMG) systems, utilizing deep learning and an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D). …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…Obstacle intrusion detection in railway systems is a critical technology for ensuring the operational safety of trains. However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. …”
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A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis by Behnaz Motamedi, Balázs Villányi

“…Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. …”
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Stochastic artificial intelligence models for water resources management: innovative riverflow estimation amidst uncertainty by Mojtaba Poursaeid

“…The primary model used was RT, a method that uses Bayesian optimization and stochastic search algorithms to provide an accurate estimate of the maximum flow within a river. …”
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Optimization model for wireless charging and power saving of smart canes for the visually impaired based on DRL by Zhaohua Ji, Cheng Xu, Jie Huang, Qinghui Zhou, Tao Yang, Diyi Zhang, Wuchao Zheng

“…Unlike conventional charging strategies that rely on static scheduling, our model dynamically optimizes charging decisions using a Deep Q-Network (DQN)-based algorithm, considering real-time environmental factors and user behavior. …”
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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids by Sungmin Lim, Jaekyu Lee, Sangyub Lee

“…This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). …”
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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…Traditional forecasting methods demonstrate evident limitations in capturing the nonlinear characteristics and complex volatility patterns of price series, underscoring the necessity of developing high-precision prediction models. This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). …”
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Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets by Yizhi Luo, Kai Lin, Zixuan Xiao, Yuankai Chen, Chen Yang, Deqin Xiao

“…In modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environments. …”
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