Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms by H. Feza Carlak, Ergin Kayar

“…The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. …”
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Spatial feature recognition and layout method based on improved CenterNet and LSTM frameworks by Yuxuan Gu, Fengyu Liu, Xiaodi Yi, Lewei Yang, Yunshu Wang

“…Consequently, this study introduces an advanced spatial feature recognition and layout methodology employing enhanced CenterNet and LSTM (Long Short-Term Memory) frameworks, which is bifurcated into two major components—first, HCenterNet-based feature recognition enhances feature extraction through an attention mechanism and feature fusion technology, refining the identification of small targets within complex background areas; second, a GA-BiLSTM (Genetic Algorithm - Bidirectional LSTM)-based spatial layout model uses a bidirectional LSTM network optimized with a genetic algorithm (GA), aimed at fine-tuning the network parameters to yield more accurate spatial layouts. …”
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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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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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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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A Bi-Level Optimization Scheme for Energy Storage Configuration in High PV-Penetrated Distribution Networks Based on Improved Voltage Sensitivity Strategy by Bo Gao, Qing Du, Gonghao Zhan, Jian Han

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Path Planning of Underground Robots via Improved A* and Dynamic Window Approach by Jianlong Dai, Yinghao Chai, Peiyin Xiong

“…To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). …”
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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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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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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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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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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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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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Improved PICEA-g-based multi-objective optimization scheduling method for distribution network with large-scale electric vehicles by Meiyi Huo, Songling Pang, Hailong Zhao

“…Abstract Large-scale electric vehicle access to the distribution grid for charging can affect the security and economic operation of the grid. In this paper, an optimal scheduling method for large-scale EV access to the distribution grid based on the improved preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) is proposed. …”
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Multi-objective programming method for ship weather routing based on fusion of A* and NSGA-II by Yuankui LI, Jiyuan SUO, Dongye YU, Xinyu ZHANG, Fang YANG, Xuefeng YANG

“…ConclusionIn summary, the proposed method can be applied to optimize ship ocean routes under multiple constraint conditions and identify routes that meet the voyage objectives, thereby reducing operational costs, improving shipping efficiency and providing support for ship meteorological navigation and future intelligent ship navigation.…”
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Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements by Cheng Chen, Guan-Nian Chen, Song Feng, Xiao-Zhen Fan, Liang-Tong Zhan, Yun-Min Chen

“…This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. …”
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Multi-Objective Dynamic System Model for the Optimal Sizing and Real-World Simulation of Grid-Connected Hybrid Photovoltaic-Hydrogen (PV-H₂) Energy Systems by Ayatte I. Atteya, Dallia Ali, Nazmi Sellami

“…The model integrates a Particle Swarm Optimisation (PSO) algorithm that enables minimising both the levelised cost of energy (LCOE) and the building carbon footprint with a dynamic model that considers the real-world behaviour of the system components. …”
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Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce by Yi Xie, Heng-Qing Ye, Wenbin Zhu

“…Experiments on real-world data show that our framework significantly outperforms baseline strategies, achieving a 14.48% increase in order volume and revenue improvements ranging from 0.19% to 43.91%. The minimum-cost flow algorithm consistently outperforms the greedy approach, especially in large-scale instances. …”
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