Search Results - (( evolution optimization _ algorithm ) OR ( variable wolf optimization algorithm ))
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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization
Published 2018“…M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). …”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic algorithm which is appealing to researcher owing to its demonstrated performance as cited in the scientific literature. …”
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Optimal power flow solutions for power system operations using moth-flame optimization algorithm
Published 2021“…In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400 $/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. …”
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Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
Published 2024“…These methods are tested using 20 test functions with different variables also with four initial points have been for each variable. …”
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A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification
Published 2024“…Initially, the Grey Wolf algorithm (GWO) is leveraged to exploit its advantages in identifying optimal parameters. …”
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Grey Wolf optimization based power management strategy for battery storage of dfig-wecs in standalone operating mode
Published 2023Conference Paper -
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A MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR WELLBORE TRAJECTORY DESIGN
Published 2021“…To address this issue, a new hybridization of cellular automata (CA) technique with grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms is proposed in this work which solves these three optimization objectives of drilling through 17 tuning variables. …”
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Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
Published 2024“…Improved mean fitness function values were also revealed in the TRS (11.63%) and EMPS (69.63%) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm).…”
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Metaheuristic optimization of perovskite solar cell performance using Taguchi grey relational analysis with grey wolf optimizer
Published 2025“…The best solutions of the MLR model are finally predicted by using GWO algorithm where both Jsc and PCE are successfully optimized to 25.67 mA/cm2 and 24.73%, respectively.…”
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Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system
Published 2024“…To build and improve the Fuzzy PID controller for the semi-active suspension system used in quarter cars, using a novel meta-heuristic technique known as Grey Wolf Optimizer (GWO). Here the magnetorheological damper (MR) fluid with the Fuzzy PID controller was examined to optimize using the GWO algorithm. …”
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A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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An application barnacles mating optimizer for forecasting of full load electrical power output
Published 2020“…The inputs are fed into the BMO algorithm which acts as a forecasting model. The performance of BMO is later compared against two comparable meta-heuristic algorithms namely Grey Wolf Optimizer (GWO) and Moth-flame Optimizer (MFO). …”
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Solving the optimal reactive power dispatch problem based on moth-flame optimizer for power system operation and planning
Published 2018“…The effectiveness of MFO is tested on IEEE-30 bus system with 25 control variables and the comparison with other recent algorithms such as grey wolf optimizer (GWO), ant-lion optimizer (ALO) and multi-verse optimizer (MVO) also will be presented in this paper.…”
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Dynamic Economic Dispatch For Power System
Published 2016“…Through an appropriate utilization of the structural features of the model, a solution algorithm based on Particle Swarm Optimization is developed. …”
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4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer
Published 2023“…Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. …”
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Two level Differential Evolution algorithms for ARMA parameters estimatio
Published 2013“…The problem of determining simultaneously the model order and coefficient of an Autoregressive Moving Average (ARMA) model is examined in this paper. An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. …”
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Proceeding Paper -
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Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. …”
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Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm
Published 2024“…Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. …”
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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