Simultaneous OPEX and carbon footprint reduction with hydrogen enhancement in autothermal reforming: a machine learning–based surrogate modeling and optimization framework by Sahar Shahriari, Davood Iranshahi

“…The resulting solutions demonstrate notable improvements across all targeted criteria. The proposed framework helped reduce the simulation cost and also achieved 65.69 % higher hypervolume and 66.26 % lower IGD than the genetic algorithm. …”
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Improved machine classification algorithm for electric rail circuits in train warning systems by I. V. Prisukhina, D. V. Borisenko

“…In this article, we present an improved classification algorithm, which combines the simplicity of implementation and accuracy. …”
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DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

“…Third, the Neck network adopts a GhostC3 structure, which reduces redundancy through linear operations, further minimizing computational costs. Fourth, to improve multi-scale adaptability, we replace the standard loss function with Alpha-IoU, enhancing model robustness. …”
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Autonomous walking path planning of underground handling robot based on improved A* and DWA algorithm by Kekun ZHANG, Jiusheng BAO, Junwei AI, Xiaoming YUAN, Yan YIN, Maosen WANG, Shirong GE

“…Then, in order to solve the problem of low search efficiency and insufficient smoothness of the planned path of the conventional A* algorithm, the exponential optimization method and the quadratic B-spline curve method are used to improve the algorithm in turn, and the performance of the improved A* algorithm is compared and analyzed. …”
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A Vehicle Path Planning Algorithm: QDDG-RRT by Ruixin Zhang, Qing Xu, Kai Sun, Yi Liu, Ximming Zhu, Guo Zhang, Xiang Cheng

“…Key improvements include dynamically constraining the search space using a direction guidance strategy, employing steering techniques to avoid obstacles, optimizing path length to minimize global cost, and refining trajectories using second-order Bessel curves. …”
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Nonlinear Hysteresis Parameter Identification of Piezoelectric Actuators Using an Improved Gray Wolf Optimizer with Logistic Chaos Initialization and a Levy Flight Variant by Yonggang Yan, Kangqiao Duan, Jianjun Cui, Shiwei Guo, Can Cui, Yongsheng Zhou, Junjie Huang, Geng Wang, Dengpan Zhang, Fumin Zhang

“…This paper proposes an improved Gray Wolf Optimization (GWO) algorithm for high-accuracy identification of hysteresis model parameters based on the Bouc–Wen (BW) differential equation. …”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Explainable Artificial Intelligence (xAI) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. …”
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Based on the Integration of the Improved A* Algorithm with the Dynamic Window Approach for Multi-Robot Path Planning by Yong Han, Changyong Li, Zhaohui An

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Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks by Manar AL-jabr, Ali Diab, Jomana AL-Diab

“…It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. …”
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Multi-Objective Ore Blending Optimization for Polymetallic Open-Pit Mines Based on Improved Matter-Element Extension Model and NSGA-II by Jun Xiang, Jianhong Chen, Aishu Zhang, Xing Zhao, Shengyuan Zhuo, Shan Yang

“…This study constructs a multi-objective ore blending optimization system for complex polymetallic open-pit mines based on the improved matter-element extension model and NSGA-II algorithm. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…The findings indicate that the RF and SVM algorithms outperform other models in predicting operational risk across all scenarios. …”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. …”
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Evaluation of Traditional and Data-Driven Algorithms for Energy Disaggregation Under Sampling and Filtering Conditions by Carlos Rodriguez-Navarro, Francisco Portillo, Isabel Robalo, Alfredo Alcayde

“…A key aspect of the evaluation was the difference in testing conditions: while traditional algorithms were evaluated under multiple experimental configurations, deep learning models, due to their extremely high computational cost, were analyzed exclusively under a specific configuration consisting of a 1-s sampling rate, with harmonic content present and without applying power filters. …”
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A Hybrid Approach for Reachability Analysis of Complex Software Systems Using Fuzzy Adaptive Particle Swarm Optimization Algorithm and Rule Composition by Nahid Salimi, Seyfollah Soleimani, Vahid Rafe, Davood Khodadad

“…Furthermore, it explored 32.7% fewer states on average than the hybrid method based on Particle Swarm Optimization and Gravitational Search Algorithms, and 57.4% fewer states compared to Genetic Algorithms, indicating improved search efficiency. …”
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Enhancing adaptive beamforming by enhanced MUSIC algorithm for urban environments in O-RAN architecture by Mustafa Mayyahi, Jordi Mongay Batalla, Constandinos X. Mavromoustakis

“…Integrating precoding functions directly within the open radio unit (O-RU) and strategically using predictive AoA modeling streamlines network operations, reduces operational costs, and improves the overall user experience. …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…This study elucidates areas where predictive ML algorithms may ideally inform GET study design to accelerate optimization and improve efficiencies upon the further training of these models.…”
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Achieving comprehensive water productivity improvement: A multi-objective simulation-optimization model for water productivity-oriented irrigation water management by Gang Li, Chenglong Zhang, Zailin Huo, Yanqi Liu

“…In this study, a multi-objective simulation-optimization model is established for maximizing irrigation water productivity (IWP), economic water productivity (EWP), and nutritional water productivity (NWP) concurrently. …”
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Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living by Mustufa Haider Abidi, Hisham Alkhalefah, Zeyad Almutairi

“…For achieving a better training and testing process, the weights in the RRNN model are optimally selected by using the hybrid algorithm named Hybrid Rat Swarm with Coati Optimization Algorithm, which is developed with the integration of the Rat Warm Optimization and Coati Optimization. …”
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Simulation study on the urban-rural integration circulatory mechanism system in China: Based on system dynamics model and multi-objective genetic algorithm by Gaoyang Liang, Mingqiang Xing, Jianqiang Zhao

“…Additionally, multi-objective optimization solutions are proposed using a Multi-Objective Genetic Algorithm (MOP-GA), which suggest that a comprehensive development strategy that balances urban-rural resource allocation achieves the highest level of integration. …”
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Modeling methylene blue removal using magnetic chitosan carboxymethyl cellulose multiwalled carbon nanotube composite with genetic algorithms and regression techniques by Mahmood Yousefi, Saeid Fallahizadeh, Yosra Maleki, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…From the feature importance X2 (initial methylene blue concentration) was the most important feature while X1 (contact time) and X4 (adsorbent amount) were not too important and can be eliminated from the models. The result of Genetic Algorithm analysis also proved the model has converged to the optimal solution effectively, the best solution of X1 = 49.41, X2 = 110.62, X3 = 11.85 and X4 = 20 which gives the maximum removal efficiency = 94.64% of methylene blue. …”
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