Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm by Xiaolei Zhang, Kaigong Zhao, Shang Gao, Changming Li

“…Additionally, a pre-allocated bee swarm algorithm is introduced to mitigate the issue of local incident points being unable to participate in rescue due to low weights, and a comparison of traditional genetic algorithms and particle swarm optimization algorithms is conducted. …”
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Solving and Optimization of Cobb–Douglas Function by Genetic Algorithm: A Step-by-Step Implementation by Ali Dinc, Faruk Yildiz, Kaushik Nag, Murat Otkur, Ali Mamedov

“…This study presents an innovative application of genetic algorithms (GAs) for optimizing the Cobb–Douglas production function, a cornerstone of economic modeling that examines the relationship between production output and the inputs of labor and capital. …”
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An Efficient Computational Algorithm for the Nonlocal Cahn–Hilliard Equation with a Space-Dependent Parameter by Zhengang Li, Xinpei Wu, Junseok Kim

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A novel star-nosed mole optimization algorithm applied for MPPT of PV systems by Ali M. Eltamaly, Zeyad A. Almutairi

“…Abstract Star-nosed mole optimization (SNMO) algorithm is a novel metaheuristic algorithm inspired by the unique foraging behavior of the star-nosed mole. …”
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Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms by Hasan Alkahtani, Theyazn H. H. Aldhyani

“…The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. …”
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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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Development of an Improved KOA Algorithm for Solving Task Allocation in Hilly Orchards With Weeding Robots by Xiaolin Xie, Hang Jin, Heng Wang, Man Xu, Cheng Zhang, Xin Jin, Zhihong Zhang

“…The results indicated that, compared to Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), and Kepler Optimization Algorithm (KOA), GKOA reduced the optimal solution cost by 10.3%, 8.2%, 7.0%, and 4.5%, respectively. …”
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Application of Machine Learning Algorithms to Predict Gas Sorption Capacity in Heterogeneous Porous Material by Tasbiha Ibad, Syed Muhammad Ibad, Haylay Tsegab, Rabeea Jaffari

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Gradual Optimization of University Course Scheduling Problem Using Genetic Algorithm and Dynamic Programming by Xu Han, Dian Wang

“…To verify the performance of the new method, this study not only tested it on real UCSP instances at Beijing Forestry University but also conducted comparative experiments with several classic algorithms, including a traditional GA, Ant Colony Optimization (ACO), the Producer–Scrounger Method (PSM), and particle swarm optimization (PSO). …”
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Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints by Joseph Agyapong Mensah, Louis Asiedu, Felix O. Mettle, Samuel Iddi

“…The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.…”
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Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. by Cheng-Wei Wang, Chao-Yang Kuo, Chi-Huang Chen, Yu-Hui Hsieh, Emily Chia-Yu Su

“…We used machine learning methods to develop prediction models for clinical pregnancy and explored how each variable affects the outcome of interest using partial dependence plots.<h4>Results</h4>Experimental results showed that the random forest algorithm outperforms logistic regression in terms of areas under the receiver operating characteristics curve. …”
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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

“…To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. …”
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Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing by Jincheng Zhou, Umesh Kumar Lilhore, Poongodi M, Tao Hai, Sarita Simaiya, Dayang Norhayati Abang Jawawi, Deema Mohammed Alsekait, Sachin Ahuja, Cresantus Biamba, Mounir Hamdi

“…This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. …”
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High Precision and Fast Inversion of Array Lateral Logging Based on BAS-LM Algorithm by NI Xiaowei, HUAN Zhipeng, XU Sihui, ZHAO Zhaoxia, CHEN Zhanglong, CHEN Jisheng

“…Based on the Oklahoma classic reservoir model, the Particle Swarm Optimization (PSO) algorithm, the Genetic Algorithm (GA), BAS algorithm, LM algorithm, and BAS-LM algorithm are used to perform the inversion of the reservoir layer resistivity, intrusion zone radius, and intrusion zone resistivity (three parameters) and compare and verify the results. …”
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Logarithmic mean optimization a metaheuristic algorithm for global and case specific energy optimization by Idriss Dagal, Ambe Harrison, Wulfran Fendzi Mbasso, Fritz Nguemo Kemdoum, Mohammad Khishe, Pradeep Jangir, Aseel Smerat, Saad F. Al-Gahtani, Z. M.S. Elbarbary, Emmanuel Fendzi Donfack, Hassan Abouobaida

“…LMO leverages logarithmic mean operations to achieve a superior balance between exploration and exploitation. The algorithm’s performance was benchmarked against six established methods—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Cuckoo Search Algorithm (CSA), and Firefly Algorithm (FA)—using the CEC 2017 suite of 23 high-dimensional functions. …”
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Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms by Tejas Joshi, Pulkit Mathur, Parita Oza, Smita Agrawal

“…The results show that ML algorithms are highly effective in predicting CS, with the random forest algorithm achieving the highest accuracy (R² = 0,95; error = 3,74). …”
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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

“…The straightforward algorithm, high accuracy and minimal computational efforts are the major advantages of this approach. …”
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Multistep PV power forecasting using deep learning models and the reptile search algorithm by Sameer Al-Dahidi, Hussein Alahmer, Bilal Rinchi, Abdullah Bani-Abdullah, Mohammad Alrbai, Osama Ayadi, Loiy Al-Ghussain

“…Further, RSA was benchmarked against the Whale Optimization Algorithm (WOA), Gray Wolf Optimizer (GWO), and Constrained Particle Swarm Optimization (CPSO), and consistently outperformed all three across all models in both forecasting horizons. …”
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Algorithm for Acoustic Wavefield in Space-Wavenumber Domain of Vertically Heterogeneous Media Using NUFFT by Ying Zhang, Shikun Dai

“…The correctness of the algorithm was verified by comparing it with the finite-element method. …”
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Research on LSTM-PPO Obstacle Avoidance Algorithm and Training Environment for Unmanned Surface Vehicles by Wangbin Luo, Xiang Wang, Fang Han, Zhiguo Zhou, Junyu Cai, Lin Zeng, Hong Chen, Jiawei Chen, Xuehua Zhou

“…In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. …”
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