Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm by M. H. Zhang, X. Feng, M. Bano, C. Liu, Q. Liu, X. Wang

“…Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. …”
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Optimum design of double tuned mass dampers using multiple metaheuristic multi-objective optimization algorithms under seismic excitation by Fateme Zamani, Sayyed Hadi Alavi, Mohammadreza Mashayekhi, Ehsan Noroozinejad Farsangi, Ataallah Sadeghi-Movahhed, Ali Majdi

“…The tuning process is carried out using a combination of Pareto front derived from seven multi-objective metaheuristic optimization algorithms with two objectives. The proposed methodology is applied to a 10-floor case study, using ground acceleration time histories to evaluate its seismic performance. …”
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Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm by Babangida Modu, Md Pauzi Abdullah, Abdulrahman Alkassem, Mukhtar Fatihu Hamza

“…The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. …”
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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…Our results indicate that this framework achieves competitive accuracy compared to conventional random search and Bayesian optimization methods. The most significant enhancement was observed in the lattice-physics dataset, achieving a 56.6% improvement in prediction accuracy, compared to improvements of 53.2% by Hyp-RL, 44.9% by Bayesian optimization, and 38.8% by random search relative to the nominal prediction. …”
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Impact of integrating type-1 distributed generation on distribution network using modified genetic algorithm and voltage stability index: a technical and cost–benefit analysis appr... by Olakunle Elijah Olabode, Daniel Oluwaseun Akinyele, Titus Oluwasuji Ajewole, Samuel Okeolu Omogoye, Akeem Abimbola Raji

“…The backward-forward sweep formed the backbone of the load flow, while the voltage stability index was used to select the suitable buses where type-1 DG was integrated for optimal performance. The criteria considered for performance evaluation were the cost of energy loss, payback time, active power loss, and network voltage profile improvement without violating the essential network constraints. …”
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Optimizing Vehicle Routing for Perishable Products with Time Window Constraints: by Rifqi Fauzi, Adi Priansyah, Paskalis Krisna Puspadewa, Syifa Maulvi Zainun Awal, Huu Tho Nguyen, Achmad Pratama Rifai

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Enhanced Search Spring Algorithm for Green Agri-Food Supply Chain Network Design by Xiaoya Hu

“…In light of these challenges, this study presents a new Enhanced Search Spring Algorithm (ESSA), which optimizes GASCN by minimizing total transportation costs and is characterized by improved solution quality and computational efficiency. …”
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Application of Multi-Objective Optimization for Path Planning and Scheduling: The Edible Oil Transportation System Framework by Chin S. Chen, Chia J. Lin, Yu J. Lin, Feng C. Lin

“…The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. …”
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Allocation of Interline Power Flow Controller-Based Congestion Management in Deregulated Power System by Muhammad Safdar Sial, Qinghua Fu, Talles Vianna Brugni

“…The IPFC model changes the power flow by injecting power into the system. One of the most important issues in reducing power losses and improving the voltage profile, which leads to a reduction in generation and congestion costs, is determining the appropriate location for installing IPFC. …”
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Relaxation Parameter Optimization in Electrical-to-Mechanical Co-Simulation Based on Time Windowing WR Technique by Md Moktarul Alam, Richard Perdriau, Mohammed Ramdani, Mohsen Koohestani

“…This paper presents an innovative approach to enhancing the time windowing waveform relaxation (WR) technique in electrical-to-mechanical co-simulation by optimizing relaxation parameters for improved performance. …”
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Optimizing capacitor size and placement in radial distribution networks for maximum efficiency by R. Arunjothi, K.P. Meena

“…Moreover, the cost savings achieved through optimal placement and sizing are substantial.…”
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AquaFlowNet a machine learning based framework for real time wastewater flow management and optimization by P. Prabu, Ala Saleh Alluhaidan, Romana Aziz, Shakila Basheer

“…These limitations often lead to inefficiencies such as energy wastage, treatment delays, and overflow incidents, negatively impacting system performance and sustainability.AquaFlowNet leverages state-of-the-art machine learning algorithms to analyze real-time data from sensors, forecast flow variations, and optimize wastewater treatment processes. …”
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Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…Improvement of tool life, enhancement of rate of production, reduction in cost of production and closeness of surface finish to that of grinding are the major goals of the work. …”
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Multi-Objective Evolution and Swarm-Integrated Optimization of Manufacturing Processes in Simulation-Based Environments by Panagiotis D. Paraschos, Georgios Papadopoulos, Dimitrios E. Koulouriotis

“…Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. …”
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Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems by Ruchika Bhakhar, Rajender Singh Chhillar

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Business Optimization of Financial Centers in Pharmaceutical Enterprises Based on Robotic Process Automation Technology by Yali Wang, Weiwei Zhou, Yingji Li, Jingqi Sun

“…The results indicate that the research designed business optimization method for pharmaceutical enterprise financial centers based on robot process automation technology significantly improves business processing efficiency, effectively controls costs, and enhances operational flexibility. …”
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Reinforcement learning algorithm for improving speed response of a five-phase permanent magnet synchronous motor based model predictive control. by Ahmed M Hassan, Jafar Ababneh, Hani Attar, Tamer Shamseldin, Ahmed Abdelbaset, Mohamed Eladly Metwally

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Optimization of delivery routes for takeout under time-varying road networks by Jiaci Wang, Liang Huang, Yuanqiao Wen, Yamin Huang, Man Zhu

“…This model is designed to effectively optimize the delivery plan, achieving a balance between delivery costs and customer satisfaction. …”
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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti

“…Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. …”
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Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine by Hanliang Bian, Zhongxun Sun, Jiahan Bian, Zhaowei Qu, Jianwei Zhang, Xiangchun Xu

“…Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. …”
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