Multi-Objective Optimization of the Rotary Turning of Hardened Mold Steel for Energy Saving and Surface Roughness Improvements by Tat Khoa Doan, Trung Thanh Nguyen, An-Le Van

“…The entropy method and vibration and communication particle swarm optimization (VCPSO) algorithm were employed to compute the weights and determine optimal factors. …”
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Multi-objective optimization to manage reservoir water quality and quantity via selective withdrawal and watershed control by Habib Abbasi, Mohammad taghi Aalami, Mohammad Hossein Niksokhan, Leila Malekan

“…Soil and Water Assessment Tool (SWAT) model was linked to a reservoir water quality simulation model (CE-QUAL-W2), the linked watershed-reservoir model was coupled with Multi-Objective Particle Swarm Optimization algorithm (MOPSO) to find the best set of decisions to optimize the reservoir outflow quality and quantity objectives. …”
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Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization by Mohammad Babar, Muhammad Sohail Khan, Ahmad Din, Farman Ali, Usman Habib, Kyung Sup Kwak

“…The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. …”
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Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT) by Hossam Hassan Ammar, Ahmad Taher Azar, Raafat Shalaby, M. I. Mahmoud

“…Moreover, three metaheuristic optimization techniques are applied; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and AntLion Optimizer (ALO) are used for tuning the FO parameters of the proposed INC-MPPT. …”
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Multi-Objective Optimization for Volt-Var Parameter Tuning: Reducing Losses and Voltage Unbalance in Distribution Systems by Arthur E. S. Ribeiro, Francisco C. R. Coelho, Wesley Peres

“…The optimization problem is solved using NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOPSO (Multi-Objective Particle Swarm Optimization), with three-phase power flow analysis performed using OpenDSS integrated into a Python-based framework. …”
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A stochastic optimization method for economic microgrid operation based on "source-load operation" mode classification by ZHOU Hang, MA Lihong, LI Zhongzhong, WANG Haisheng, LI Sifan, ZHANG Changjun, FU Linbei

“…Finally， the adaptive weighted particle swarm optimization （AW-PSO） is employed to solve the stochastic optimization model for microgrid operations. …”
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Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh, Abdulrahman Alassi, Nader Meskin, Mohammed A. Al-Hitmi, Ahmed M. Massoud

“…We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. …”
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Energy Efficiency Optimization for UAV Distribution and Resource Allocation in NOMA and Multi-UAV Assisted Wireless Networks by Yuan Ren, Jieyu Wang, Yulong Xiao, Xuewei Zhang, Fan Jiang, Junxuan Wang, Jing Jiang, Guangyue Lu

“…At the same time, each GU group is assigned a serving UAV based on improved particle swarm optimization (PSO) algorithm. A system energy efficiency (EE) maximization problem is then formulated by jointly optimizing the UAV space locations as well as the transmit powers of GUs and UAVs. …”
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Optimization and analysis of interior permanent magnet drive motor with unequal thickness magnetic poles for electric vehicle by Shilun MA, Yongyi WU, Changwei LI, Keqi CHEN

“…Finally, with torque ripple, cogging torque, and output torque as optimization objectives, the particle swarm optimization algorithm was used to perform multi-objective optimization on the maximum magnetic pole thickness, minimum magnetic pole thickness, the width of magnetic pole, the depth and width of the rotor auxiliary slots, in order to determine the optimal parameters, and a prototype was then developed for experimental testing. …”
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Gray Wolf Optimizer With Communication Strategy Based on DV-Hop for Nodes Location of Wireless Sensor Networks by Dan Yu, Ting Yuan, Haiyin Qing, Wenwu Xie, Peng Zhu

“…Comprehensive experimental analyses on established benchmark functions have validated the efficacy of the IGWO-CS method, illustrating its competitiveness with other advanced algorithms such as the Ranking-based Adaptive Cuckoo Search (RACS) and Modified Particle Swarm Optimization (MPSO). …”
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Stackelberg Game Optimization Strategy of Virtual Power Plants and Electric Vehicles Based on Conditional Value-at-Risk by MA Qianxin, JIA Heping, GUO Yuchen, LI Peijun, YANG Ye, LIU Dunnan, ZHAO Zhenyu

“…The lower-level model minimized user costs by responding to these prices using a utility function that captures both cost satisfaction and charging experience. A particle swarm optimization algorithm was employed to solve the coupled model and identify the equilibrium strategies.…”
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Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine by Deliang Liu, Biao Lu, Wenping Wu, Wei Zhou, Wansu Liu, Yiye Sun, Shilong Wu, Guolong Shi, Leiming Yuan

“…The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). …”
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Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization by Medhat A. Tawfeek, Ibrahim Alrashdi, Madallah Alruwaili, Leila Jamel, Gamal Farouk Elhady, Haitham Elwahsh

“…We compare the performance of the proposed MACOA with existing state-of-the-art techniques, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony, Deep Reinforcement Learning, and Energy Reliable ACO Routing Protocol (E-RARP) in terms of network lifetime, network stabilization time, energy efficiency, load balancing, and throughput. …”
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Performance Optimization Method of Steam Generator Liquid Level Control Based on Hybrid Iterative Model Reconstruction by Xiaoyu Li, Xiangsong Kong, Changqing Shi, Jinguang Shi, Zean Yang

“…The method first constructs the initial dataset with a small-sample Latin-square experiment design and then builds two different fitting models, SVM and Kriging, based on the initial dataset respectively, under the hybrid model fusion idea. After that, the particle swarm optimization algorithm is used to calculate the optimal point of the current valid model, and the optimization process is controlled by establishing the iteration termination judgment based on the historical iteration data. …”
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Integrated optimization and coordination of cascaded reservoir operations: Balancing flood control, sediment transport and ecosystem service by Xinmiao Cao, Teng Lin, Jiahui Li, Ting Zhou

“…To address the optimization model, an elite mutation‐based multi‐objective particle swarm optimization (MOPSO) algorithm that integrates genetic algorithms (GA) is developed. …”
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Failure thresholds and weak part identification in cascade reservoir system: A risk-based optimization framework by Haibin Wang, Jiahong Liu, Chao Mei, Jia Wang, Tianxu Song

“…Secondly, a dynamic Particle Swarm Optimization-Genetic Algorithm (PSO-GA) model optimizes outflow strategies during flood events. …”
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Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization by Rajeev Goel, Sandhya Bansal, Kavita Gupta

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Efficient multi-UAV path planning in dynamic and complex environments using hybrid polar lights optimization by Ziyin Xu, Zhilin Wang, Rui Liu, Chenliang Huang, Yuxiang Shi, Mingjing Wang, Huiling Chen

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Queen honey bee migration (QHBM) optimization for droop control on DC microgrid under load variation by Aripriharta Aripriharta, Mochammad Syarifudin Al Rasyid, Muhammad Cahyo Bagaskoro, Irham Fadlika, Sujito Sujito, Arif Nur Afandi, Saodah Omar, Norzanah Rosmin

“…The optimizcv ation considers reference voltage constraints and virtual impedance at various load conditions, particularly resistive. The optimization problem is addressed using two techniques: queen honey bee migration (QHBM) and particle swarm optimization (PSO). …”
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Adaptive continuous-discrete variables optimization for active learning with extremely sparse data in optical material design by Serang Jung, Eungkyu Lee

“…In the adaptive scheme, we examine the performance of three machine learning (ML) models—Gaussian process regression, factorization machines (FM), and field-aware FM—for a surrogate function, and ML model-specific optimization algorithms such as discrete particle swarm optimization, artificial bee colony optimization, and simulated annealing. …”
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