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Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique.

, , , and . Appl. Soft Comput., (2017)

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Product Recommendation using Deep Learning in Computer Vision., , , and . ICSECS, page 263-267. IEEE, (2023)Solving the Optimal Reactive Power Dispatch Problem Based on Moth-Flame Optimizer for Power System Operation and Planning., , , , , and . SNPD, page 275-280. IEEE Computer Society, (2018)Using the gray wolf optimizer for solving optimal reactive power dispatch problem., , , and . Appl. Soft Comput., (2015)An Improved Optimization Algorithm-Based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid Machine Learning Approach., , , and . TCCE, page 209-223. (2022)Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique., , , and . Appl. Soft Comput., (2017)Evolutionary mating algorithm., , , , and . Neural Comput. Appl., 35 (1): 487-516 (2023)Optimal Finite- Time Prescribed Performance of Servo Pneumatic Positioning with PID Control Tuning using an Evolutionary Mating Algorithm., , and . ICCSCE, page 196-201. IEEE, (2023)Non-Regularized Reconstruction of Magnetic Moment Distribution of Magnetic Nanoparticles using Barnacles Mating Optimizer., , , , and . ICSSE, page 533-536. IEEE, (2023)Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application., , , and . Math. Comput. Simul., (April 2024)Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems., , , and . Eng. Appl. Artif. Intell., (2020)