Optimal Reactive Power Generation for Radial Distribution Systems Using a Highly Effective Proposed Algorithm by Le Chi Kien, Thuan Thanh Nguyen, Bach Hoang Dinh, Thang Trung Nguyen

“…In this paper, a proposed modified stochastic fractal search algorithm (MSFS) is applied to find the most appropriate site and size of capacitor banks for distribution systems with 33, 69, and 85 buses. …”
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Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony Models by Zhonghua Jiang, Qianlong Xia, Zhizhou Wang, Kaiwei Zhu, Qianyu Su, Jiajun Wang, Yirui Huang, Bo Wu, Yan Hong

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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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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…<b>Objectives:</b> The primary objective of this study is to assess whether integrating autoencoder-derived features into traditional ML models can improve their performance in predicting tumor dynamics three months post-GKRS in patients with brain metastases. …”
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Classification Based on Brain Storm Optimization With Feature Selection by Yu Xue, Yan Zhao, Adam Slowik

“…Recently, some evolutionary algorithms (EAs) such as the fireworks algorithm (FWA) and brain storm optimization (BSO) algorithm have been employed to implement the evolutionary classification model and achieved the desired results. …”
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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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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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A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18 by Peiyang Wei, Can Hu, Jingyi Hu, Zhibin Li, Wen Qin, Jianhong Gan, Tinghui Chen, Hongping Shu, Mingsheng Shang

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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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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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Integration of electric vehicle charging stations with distributed generation using multi-objective metaheuristic optimization by Aya Desoky Gaber, E.M. Abdallah, M.I. Elsayed, Ahmed Abdelbaset

“…This paper uses a multi-objective optimization approach metaheuristic algorithm, specifically the Whale Optimization Algorithm (WOA), Zebra Optimization Algorithm (ZOA), and Puma Optimization Algorithm (POA), to determine the optimal size and placement of DG units in the presence of EVCS. …”
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Usage of the optimal fuzzy system models on chloride diffusion of concrete by Shuai Sun

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A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions by Boyan Huang, Kai Song, Shulin Jiang, Zhenqing Zhao, Zhiqiang Zhang, Cong Li, Jiawen Sun

“…Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.…”
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A Ultra-Low Cost and Accurate AMC Algorithm and Its Hardware Implementation by Yuqin Zhao, Tiantai Deng, Bill Gavin, Edward A. Ball, Luke Seed

“…In this design, the CAMC algorithm is optimized to fit the FPGA characteristics to further improve the performance, and the computing demands of which could be saved over 94&#x0025; compared with other state-of-the-art designs. …”
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TBESO-BP: an improved regression model for predicting subclinical mastitis by Kexin Han, Yongqiang Dai, Huan Liu, Junjie Hu, Leilei Liu, Zhihui Wang, Liping Wei

“…In comparison to six alternative models, the TBESO-BP model demonstrates superior accuracy and lower error values.DiscussionThe TBESO-BP model emerges as a precise tool for predicting subclinical mastitis in dairy cows. The TBESO algorithm notably enhances the efficacy of the BP neural network in regression prediction, ensuring elevated computational efficiency and practicality post-improvement.…”
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Aircraft range fuel prediction study based on WPD with IAPO optimized BiLSTM–KAN model by Weizhen Tang, Jie Dai, Yuantai Li

“…Additionally, the SPM chaotic mapping strategy is utilized for population initialization, while the introduction of the golden sine operator variation strategy enhances the local search capabilities of the algorithm. The adaptive swoop switching strategy adjusts the search intensity, thereby improving the global search performance and convergence speed of the Arctic Puffin Optimization (APO). …”
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Leveraging Radiomics and Genetic Algorithms to Improve Lung Infection Diagnosis in X-Ray Images Using Machine Learning by A. Beena Godbin, S. Graceline Jasmine

“…A comparative analysis is conducted among the genetic algorithm-based TPOT (Tree-based Pipeline Optimization Tool) settings, namely TPOT-Default, TPOT-Light, and TPOT-Sparse, to select the most effective hyperparameters. …”
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Filter-Based Feature Selection Using Information Theory and Binary Cuckoo Optimisation Algorithm by Ali Muhammad Usman, Umi Kalsom Yusof, Maziani Sabudin

“…Both were used together with binary cuckoo optimization algorithm BCOA (BCOA-MI and BCOA-EI). The target is to improve classification performance (reduce the error rate and computational complexity) on eight datasets with varying degrees of complexity. …”
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