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LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting
Published 2015“…Realized in commodity time series data, the proposed technique is compared against two comparable techniques, including single GWO and LSSVM optimized by Artificial Bee Colony (ABC) algorithm (ABC-LSSVM). …”
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Time series forecasting of energy commodity using grey wolf optimizer
Published 2015“…The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices.In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting.The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price.Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE).Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. …”
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Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer
Published 2015“…In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting. …”
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Data normalization techniques in swarm-based forecasting models for energy commodity spot price
Published 2014“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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Robust multi-user detection based on hybrid grey wolf optimization
Published 2020“…The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than that of genetic algorithm, differential evolution algorithm and Grey wolf optimization algorithm, and has the lower BER.…”
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Book Chapter -
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Moreover, another new variant of CGWO, namely opposition based competitive grey wolf optimizer (OBCGWO), is proposed to enhance the performance of CGWO in feature selection. …”
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A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
Published 2017“…Purpose – This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). …”
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An Application of Grey Wolf Optimizer for Commodity Price Forecasting
Published 2015“…Over the recent decades, there are many nature inspired optimization algorithms have been introduced. In this study, a newly algorithm namely Grey Wolf Optimizer (GWO) is employed for gasoline price forecasting. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…—Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
Published 2019“…Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. …”
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Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm
Published 2018“…In this research, swarming intelligence is used to solve optimisation problem. Grey Wolf Optimizer and Dragonfly Algorithm were chosen. …”
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Monograph -
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An application of grey wolf optimizer for commodity price forecasting
Published 2015“…Over the recent decades, there are many nature inspired optimization algorithms have been introduced.In this study, a newly algorithm namely Grey Wolf Optimizer (GWO) is employed for gasoline price forecasting.The performance of GWO is compared against the results produced by Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE) algorithm. …”
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An improved grey wolf optimizer with hyperbolic tangent updating mechanism for solving optimization problems
Published 2021“…The original algorithm of Grey Wolf Optimizer (GWO) has a common problem which is too soon to trap in local optima. …”
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Grey Wolf Optimizer for Solving Economic Dispatch Problems
Published 2014“…This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus). …”
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Hybrid conjugate gradient methods using strong Wolfe line search for Whale Optimization Algorithm / Wan Nur Athirah Wan Mohd Zakirudin
Published 2023“…Lastly, the implementation of LAMR-HS in the Whale Optimization Algorithm (WOA) aims to enhance the convergence speed, ultimately demonstrating the successful hybridization between the two algorithms.…”
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Thesis -
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An improved grey wolf optimization based MPPT algorithm for photovoltaic systems under diverse partial shading conditions
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