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A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem

Computers, Materials & Continua 2021年第5期67卷 2227-2242页

作者： M.Premkumar R.Sowmya pradeep jangir Kottakkaran Sooppy Nisar Mujahed Aldhaifallah Department of Electrical and Electronics Engineering GMR Institute of TechnologyRajam532127India Department of Electrical and Electronics Engineering National Institute of TechnologyTiruchirappalli620015India Rajasthan Rajya Vidyut Prasaran Nigam Sikar305001India Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia Systems Engineering Department King Fahd University of Petroleum&MineralsDhahranSaudi Arabia

The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization *** this paper,the EO,GWO,and WO algorithms are applied individually for a brushless direct current(BLDC)design optimization *** EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium *** GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale,*** primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total ***,two objective functions are being used to achieve these *** first refers to a design with high power output and *** second is a constraint imposed by the reality that the motor is built into the wheel of the vehicle and,therefore,a lightweight is *** EO,GWO,and WOA algorithms are then utilized to optimize the BLDC motor’s design variables to minimize the motor’s total mass or maximize the motor efficiency by simultaneously satisfying the six inequality *** simulation is carried out using MATLAB simulation software,and the simulation results prove the dominance of the proposed *** paper also suggests an efficient method from the proposed three methods for the BLDC motor design optimization problem.

关键词： BLDC motor constrained equilibrium optimizer singleobjective optimization

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

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Computers, Materials & Continua 2022年第2期70卷 2435-2452页

作者： M.Premkumar pradeep jangir B.Santhosh Kumar Mohammad A.Alqudah Kottakkaran Sooppy Nisar Department of Electrical and Electronics Engineering GMR Institute of TechnologyRajam532127Andhra PradeshIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Department of Computer Science and Engineering GMR Institute of TechnologyRajam532127Andhra PradeshIndia German Jordanian University Amman11180Jordan Department of Mathematics College of Arts and SciencesPrince Sattam Bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

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 ***,the BLDC motor design problem is considered to be an optimization *** this paper,the analytical model of the BLDC motor is presented,and it is considered to be a basis for emphasizing the optimization *** 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 in electromagnetic ***-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic *** bio-inspired multi-objective grey wolf optimizer(MOGWO)is presented in this paper,and it is formulated based on Pareto optimality,dominance,and archiving *** performance of theMOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design *** results proved that the proposedMOGWO algorithm could handle nonlinear constraints in electromagnetic optimization *** performance comparison in terms of Generational Distance,inversion GD,Hypervolume-matrix,scattered-matrix,and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected *** source code of this paper is backed up with extra online support at https://***/mysite and https://***/matlabcentral/fileexchange/75259-multiobjective-non-sorted-grey-wolf-mogwo-nsgwo.

关键词： BLDC motor electromagnetics metaheuristic multi-objective grey wolf optimizer

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BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

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Computers, Materials & Continua 2022年第1期70卷 557-579页

作者： R.Manjula Devi M.Premkumar pradeep jangir B.Santhosh Kumar Dalal Alrowaili Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical and Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Department of Computer Science and Engineering Guru Nanak Institute of TechnologyHyderabad501506TelanganaIndia Mathematics Department College of ScienceJouf UniversitySakakaP.O.Box:2014Saudi Arabia Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization *** the run time increases exponentially,FS is treated as an NP-hard *** researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization *** paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large *** proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary *** findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art *** results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification *** proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 *** low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

关键词： Binary optimization feature selection machine learning hunger games search optimization

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IRKO:An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

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Computers, Materials & Continua 2022年第3期70卷 4803-4827页

作者： R.Manjula Devi M.Premkumar pradeep jangir Mohamed Abdelghany Elkotb Rajvikram Madurai Elavarasan Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical&Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Mechanical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityEgypt Clean and Resilient Energy Systems(CARES)Laboratory Texas A&MUniversityGalveston77553TXUSA College of Arts and Sciences Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so *** order to solve optimization problems,metaheuristic algorithms are *** of these techniques are influenced by collective knowledge and natural *** is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain ***,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization *** IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO *** performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization *** outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO *** to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization *** runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.

关键词： Engineering design global optimization local escaping operator metaheuristics Runge-Kutta optimization algorithm

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