Coverage Optimization Technology of Power Pipe Gallery Based on Improved Gray Wolf Algorithm by Cheng ZHONG, Di ZHAI, Yang LU, Xiaobo LIU, Xinru WANG, Xiongwen ZHAO

“…A comparative simulation analysis is made of various coverage optimization methods, and the results show that the proposed algorithm has robust search capabilities and it can significantly improve the network coverage performance in the narrow underground power pipe galleries, while effectively satisfying the communication needs of the monitored nodes.…”
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Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies by MD Shahriar Mahmud Bhuiyan, MD AL Rafi, Gourab Nicholas Rodrigues, MD Nazmul Hossain Mir, Adit Ishraq, M.F. Mridha, Jungpil Shin

“…This systematic literature review explores recent advancements in the application of DL algorithms to algorithmic trading with a focus on optimizing financial market predictions. …”
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A Novel Method for News Recommendation on Websites Using the Clustered-Vectors Optimization Algorithm by Piyanuch Chaipornkaew, Thepparit Banditwattanawong

“…This research introduces a recommendation model for news content utilizing a novel machine learning technique known as the “Clustered-Vectors Optimization (CVO)” algorithm. The proposed algorithm aims to optimize the clustering of news titles to improve the performance of news recommendations. …”
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An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks by Taiwo Blessing Ogunseyi, Gogulakrishan Thiyagarajan

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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…This study lays the groundwork for future research by suggesting the exploration of additional optimization algorithms and more complex neural network architectures to further improve prediction accuracy.…”
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Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization by Dedy Rahman Wijaya, Nailatul Fadhilah Syarwan, Muhammad Agus Nugraha, Dahliar Ananda, Tora Fahrudin, Rini Handayani

“…This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. …”
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A Lightweight Pavement Defect Detection Algorithm Integrating Perception Enhancement and Feature Optimization by Xiang Zhang, Xiaopeng Wang, Zhuorang Yang

“…To address the current issue of large computations and the difficulty in balancing model complexity and detection accuracy in pavement defect detection models, a lightweight pavement defect detection algorithm, PGS-YOLO, is proposed based on YOLOv8, which integrates perception enhancement and feature optimization. …”
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Flow direction algorithm for optimal coordination of directional overcurrent relays considering arc flash by Dina K. Mahmoud, Shady H．E．Abdel Aleem, Ahmed M. Ibrahim, Mahmoud M. Sayed, Wael Abdelfattah

“…This paper presents a novel meta-heuristic optimization method called flow direction algorithm (FDA) to achieve optimal coordination of Directional Overcurrent Relays (DOCRs) while considering the impact of Arc Flash (AF). …”
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A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm by Sanyi Li, Wenjie Hou, Peng Liu, Weichao Yue, Qian Wang

“…This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. …”
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Path Planning Optimization of Smart Vehicle With Fast Converging Distance-Dependent PSO Algorithm by Muhammad Haris, Haewoon Nam

“…The Particle Swarm Optimization (PSO) algorithm is widely used for optimization problems due to its simplicity and efficiency. …”
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Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning by Chaoli Tang, Wenyan Li, Tao Han, Lu Yu, Tao Cui

“…For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm’s possibility of falling into local optimal solutions during path planning. …”
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Efficiency management of engineering projects based on particle swarm multi objective optimization algorithm by Xiu Luo, Zhouxin Yi

“…A research proposes an optimization method for engineering project efficiency management based on multi-objective particle swarm optimization algorithm. …”
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Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm by Youtao Xue, Yuxin Yang, Shaobo Yao, Wenwen Zhao, Lihua Chen

“…Compared to the conventional GPR-based SBO approach, our AKC-GPR framework significantly reduces the number of required computational fluid dynamics (CFD) simulations by 27.7% while improving drag reduction by 2.83%. Qualitative and approximate comparative analysis of the results with other research groups on the same benchmark case validates the robustness and effectiveness of our proposed optimization framework.…”
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Development of algorithms for constructing two-dimensional optimal boundary-adaptive grids and their software implementation by A. E. Chistyakov, V. V. Sidoryakina, S. V. Protsenko

“…The objective of this study is the development, construction and software implementation of methods for constructing computational two-dimensional optimal boundary-adaptive grids for complex configuration regions while maintaining the specified features of the shape and boundary of the region. …”
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Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms by Bartłomiej Baran, Tomasz Rymarczyk, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Cieplak, Marcin Gąsior, Marcin Marczuk, Edmund Wąsik, Konrad Gauda

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An optimized cubic B-spline algorithm for high-precision approximation of nonlinear transport phenomena by Rabia Noureen, Muhammad Kashif Iqbal, Maryam Asgir, Bandar Almohsen, Muhammad Azeem, Husam A. Neamah

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Investigation of technoeconomic optimization for sizing renewable energy systems using metaheuristic and hybrid algorithms by Bouafia Mohammed, El Fathi Amine, El Akchioui Nabil

“…This study focuses on optimizing grid-connected Hybrid Renewable Energy Systems (HRES) in Morocco using technoeconomic approaches, including metaheuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Artificial Bee Colony (ABC) and hybrid methods combining these latter with Modified Marquardt Gradient Descent (MGD). …”
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Gradual Optimization of University Course Scheduling Problem Using Genetic Algorithm and Dynamic Programming by Xu Han, Dian Wang

“…The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, students, and classrooms. …”
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Compare of Transient Quality in Automatic Control Systems with Classic PID Algorithm and Optimal Regulator by G. T. Kulakov, K. I. Artsiomenka

“…Currently, about 90–95% of generic controllers use the PID algorithm to generate control actions, while 64% of the PID controllers are used in single-circuit automatic control systems. …”
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Optimization Design Method of Pipe-Insulating Joints Based on Surrogate Model and Genetic Algorithm by Chen Guo, Zheng Yang, Jianbo Dong, Yanchao Yue, Linjun Tian, Ping Ma

“…This study also provides examples verifying the accuracy and reliability of the surrogate model and genetic algorithm. In these examples, the maximum stress under the design dimensions given by the optimization algorithm has a maximum error of 8.98% and an average error of 4.63% compared to the preset maximum stress target, while the stress predicted by the surrogate model has a maximum error of 9.65% and an average error of 5.33% compared to the actual stress. …”
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