Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment by Wentian Shang, Jinzhang Jia

“…Abstract Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. …”
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Developing a Machine Learning-Driven Model that Leverages Meta-Heuristic Algorithms to Forecast the Load-Bearing Capacity of Piles by Tianhua Zhou

“…Additionally, it uses two separate meta-heuristic optimization methods, namely the Golden Jackal optimization algorithm (GJO) and Smell Agent Optimization (SAO), to achieve the best possible results. …”
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Machine learning-based coalbed methane well production prediction and fracturing parameter optimization by HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan

“…The model employs a random forest algorithm integrated with a multi-task learning strategy and utilizes a particle swarm optimization (PSO) algorithm to optimize fracturing parameters. …”
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Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm by A. M. Bramm, A. I. Khalyasmaa, S. A. Eroshenko, P. V. Matrenin, N. A. Papkova, D. A. Sekatski

“…The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied.…”
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Design and Analysis of SSSC-Based Damping Controller: A Novel Modified Zebra Optimization Algorithm Approach by Rajendra Kumar Khadanga, Deepa Das, Sidhartha Panda

“…The suggested study distinguishes between the mZOA method, traditional ZOA, and conventional PSO algorithms. Based on the simulation findings, it can be inferred that the modified approach that has been suggested is the most effective method for defining the said damping controller by considering the percentage improvement in the objective function value.…”
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Design of dual-layer heater based on genetic algorithm to optimize magnetic field gradient in vapor cell by Zhicheng Tan, Jing Zhu, Yanyan Liu, Siyang Lu, Lianqing Zhu

“…The parameter combinations were then optimized synchronously using genetic algorithms to reduce the magnetic field gradient in the vapor cell region and enhance the magnetic noise self-suppression capability of the heater. …”
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Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters by Hauwau Abdulrahman Aliyu, Ibrahim Olawale Muritala, Habeeb Bello-Salau, Salisu Mohammed, Adeiza James Onumanyi, Ore-Ofe Ajayi

“…Leveraging Particle Swarm Optimization (PSO) algorithm for diabetes data balancing and a genetic algorithm to select the optimal architecture for various machine learning classifiers. …”
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Applications of Metaheuristic Algorithms in Solar Air Heater Optimization: A Review of Recent Trends and Future Prospects by Jean De Dieu Niyonteze, Fumin Zou, Godwin Norense Osarumwense Asemota, Walter Nsengiyumva, Noel Hagumimana, Longyun Huang, Aphrodis Nduwamungu, Samuel Bimenyimana

“…Therefore, this paper clearly shows that the use of all six proposed metaheuristic algorithms results in significant efficiency improvements through the selection of the optimal design set and operating parameters for SAHs. …”
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An Overlapping IBM-PISO Algorithm with an FFT-Based Poisson Solver for Parallel Incompressible Flow Simulations by Jiacheng Lian, Qinghe Yao, Zichao Jiang

“…This study addresses computational challenges in the immersed boundary method (IBM) with the pressure implicit with split operator (PISO) algorithm for simulating incompressible flows. We introduce a novel time-step splitting method to implement communication overlapping optimization, aiming to reduce costs dominated by the pressure Poisson solver. …”
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Boosting feature selection efficiency with IMVO: Integrating MVO and mutation-based local search algorithms by Maryam Askari, Farid Khoshalhan, Hodjat Hamidi

“…In this research, we introduce the Improved Multi-Verse Optimizer (IMVO) algorithm, a novel feature selection method that integrates the Multi-Verse Optimizer (MVO) with local search algorithms (LSAs). …”
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Spatial–degree of freedom improvement of interference alignment in multi-input, multi-output interference channels by Yi-bing Li, Xue-ying Diao, Qian-hui Dong

“…As we know, the degree of freedom approximates the capacity of a network. To improve the achievable degree of freedom in the K -user interference network, we propose a rank minimization interference minimization algorithm. …”
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Enhancing image retrieval through optimal barcode representation by Rasa Khosrowshahli, Farnaz Kheiri, Azam Asilian Bidgoli, H. R. Tizhoosh, Masoud Makrehchi, Shahryar Rahnamayan

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Intelligent bulk cargo terminal scheduling based on a novel chaotic-optimal thermodynamic evolutionary algorithm by Shida Liu, Qingsheng Liu, Li Wang, Xianlong Chen

“…The proposed method is adept at achieving multiobjective optimization, simultaneously improving the service level and reducing costs for bulk cargo terminals, which in turn boosts their competitiveness. …”
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Modelling and optimization of well hole cleaning using artificial intelligence techniques by Nageswara Rao Lakkimsetty, Hassan Rashid Ali Al Araimi, G. Kavitha

“…This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. …”
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Optimal Placement of Leakage Sensors in Urban Gas Networks Based on an Ant Colony Algorithm and System Clustering by Zhewen Sui, Xiaobing Yuan, Baoping Cai, Fangqi Ye, Qingqing Duan, Zhiqiang Zhao, Xiaoyan Shao, Xin Zhou, Zhiming Hu

“…In urban gas network leakage monitoring, the optimized placement of sensors plays a pivotal role in ensuring public safety and minimizing system maintenance costs. …”
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Sustainable supply chain: An optimization and resource efficiency in additive manufacturing for automotive spare part by Divya Chauhan, Anubhav Pratap Singh, Anand Chauhan, Ritu Arora

“…The incorporation of additive manufacturing within the automotive supply chain holds the potential to improve operational efficiency and generate significant cost savings while enhancing sustainability. …”
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Adaptive k-means clustering algorithm based on grid and domain centroid weight by Wei CAI, Guangyu SUN, Fei YANG, Bo YAO, Chuanxian LI

“…Furthermore, when the clustering accuracy exceeded 99％ compared to the k-means algorithm, the computational efficiency was significantly improved. …”
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Charging path optimization in mobile wireless rechargeable sensor networks by Quanlong NIU, Riheng JIA, Minglu LI

“…The wireless power transfer technique is promising in solving the energy bottleneck of sensor nodes in wireless sensor networks, which can thus prolong the network lifetime or even maintain sustainable network operations.Most existing works focused on optimizing the static chargers’ deployment or mobile chargers’ charging path for static sensor nodes with fixed sensor node positions, ignoring the scenario with mobile sensor nodes.Thus, design and optimize the charging path of a mobile charger was studied for dynamic wireless sensor networks with mobile sensor nodes, to maximize the charging utility within a finite time horizon, that is, the charger can encounter as more sensor nodes as possible in a limited time and charge them.Notice that the mobile charger may stop to simultaneously charge multiple nodes within its charging range during its charging tour.The proposed charging path optimization problem was proven to be an APX-hard problem.Then, based on the constructed directed acyclic graph using discretization method, a layer-wise pruning algorithm based on the backtracking method was proposed.The proposed algorithm took the solution generated by the greedy algorithm as the benchmark and searched the optimal charging path under a fixed time division by layer-wise pruning.Simulation results show that the proposed algorithm can effectively improve the charging utility .…”
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Charging path optimization in mobile wireless rechargeable sensor networks by Quanlong NIU, Riheng JIA, Minglu LI

“…The wireless power transfer technique is promising in solving the energy bottleneck of sensor nodes in wireless sensor networks, which can thus prolong the network lifetime or even maintain sustainable network operations.Most existing works focused on optimizing the static chargers’ deployment or mobile chargers’ charging path for static sensor nodes with fixed sensor node positions, ignoring the scenario with mobile sensor nodes.Thus, design and optimize the charging path of a mobile charger was studied for dynamic wireless sensor networks with mobile sensor nodes, to maximize the charging utility within a finite time horizon, that is, the charger can encounter as more sensor nodes as possible in a limited time and charge them.Notice that the mobile charger may stop to simultaneously charge multiple nodes within its charging range during its charging tour.The proposed charging path optimization problem was proven to be an APX-hard problem.Then, based on the constructed directed acyclic graph using discretization method, a layer-wise pruning algorithm based on the backtracking method was proposed.The proposed algorithm took the solution generated by the greedy algorithm as the benchmark and searched the optimal charging path under a fixed time division by layer-wise pruning.Simulation results show that the proposed algorithm can effectively improve the charging utility .…”
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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

“…A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. …”
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