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  • Open Access


    A Grey Wolf Optimized 15-Level Inverter Design with Confined Switching Components

    S. Caroline1,*, M. Marsaline Beno2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1753-1769, 2022, DOI:10.32604/iasc.2022.020440

    Abstract Multilevel inverters are a new class of dc-ac converters designed for high-power medium voltage and power applications as they work at high switching frequencies and in renewable applications by avoiding stresses like dv/dt and has low harmonic distortion in their output voltage. In variable speed drives and power generation systems, the use of multilevel inverters is obligatory. To estimate the switching positions in inverter configuration with low harmonic distortion value, a fast sequential optimization algorithm has been established. For harmonic reduction in multilevel inverter design, a hybrid optimization technique combining Firefly and the Genetic algorithm was used. In several real-time… More >

  • Open Access


    Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem

    M. Premkumar1, Pradeep Jangir2, B. Santhosh Kumar3, Mohammad A. Alqudah4, Kottakkaran Sooppy Nisar5,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2435-2452, 2022, DOI:10.32604/cmc.2022.016488

    Abstract The first step in the design phase of the Brushless Direct Current (BLDC) motor is the formulation of the mathematical framework and is often used due to its analytical structure. Therefore, the BLDC motor design problem is considered to be an optimization problem. In this paper, the analytical model of the BLDC motor is presented, and it is considered to be a basis for emphasizing the optimization methods. The analytical model used for the experimentation has 78 non-linear equations, two objective functions, five design variables, and six non-linear constraints, so the BLDC motor design problem is considered as highly non-linear… More >

  • Open Access


    An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

    Tuğrul Çavdar1, Faruk Baturalp Günay2,*, Nader Ebrahimpour1, Muhammet Talha Kakız3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1197-1222, 2022, DOI:10.32604/iasc.2022.020127

    Abstract Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an… More >

  • Open Access


    Design of Neural Network Based Wind Speed Prediction Model Using GWO

    R. Kingsy Grace1,*, R. Manimegalai2

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 593-606, 2022, DOI:10.32604/csse.2022.019240

    Abstract The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is used to optimize the parameters… More >

  • Open Access


    Estimation of Locational Marginal Pricing Using Hybrid Optimization Algorithms

    M. Bhoopathi1,*, P. Palanivel2

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 143-159, 2022, DOI:10.32604/iasc.2022.017705

    Abstract At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is utilized in restructured electricity markets.… More >

  • Open Access


    Grey Wolf Optimization Based Tuning of Terminal Sliding Mode Controllers for a Quadrotor

    Rabii Fessi1, Hegazy Rezk2,3,*, Soufiene Bouallègue1,4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2265-2282, 2021, DOI:10.32604/cmc.2021.017237

    Abstract The research on Unmanned Aerial Vehicles (UAV) has intensified considerably thanks to the recent growth in the fields of advanced automatic control, artificial intelligence, and miniaturization. In this paper, a Grey Wolf Optimization (GWO) algorithm is proposed and successfully applied to tune all effective parameters of Fast Terminal Sliding Mode (FTSM) controllers for a quadrotor UAV. A full control scheme is first established to deal with the coupled and underactuated dynamics of the drone. Controllers for altitude, attitude, and position dynamics become separately designed and tuned. To work around the repetitive and time-consuming trial-error-based procedures, all FTSM controllers’ parameters for… More >

  • Open Access


    Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

    Xianghui Lu1, Junliang Fan2, Lifeng Wu1,*, Jianhua Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004

    Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More >

  • Open Access


    Grey Wolf Optimizer to Real Power Dispatch with Non-Linear Constraints

    G. R. Venkatakrishnan1,*, R. Rengaraj2, S. Salivahanan3

    CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.1, pp. 25-45, 2018, DOI:10.3970/cmes.2018.115.025

    Abstract A new and efficient Grey Wolf Optimization (GWO) algorithm is implemented to solve real power economic dispatch (RPED) problems in this paper. The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints. Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions. The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism. The leadership hierarchy is simulated using… More >

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