Cuckoo search as a tool for optimal design of PM brushless DC motor

Cvetkovski, Goga; Petkovska, Lidija; Lefley, Paul

doi:10.1108/compel-01-2018-0025

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Rather, they are reproductive predators. In order to propagate their species, female cuckoos slip their eggs into the nests of different species of birds [21].…”

Section: The Reproductive Behavior Of the Cuckoo In The Natural Environmentmentioning

confidence: 99%

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

2021

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This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed.

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“…Rather, they are reproductive predators. In order to propagate their species, female cuckoos slip their eggs into the nests of different species of birds [21].…”

Section: The Reproductive Behavior Of the Cuckoo In The Natural Environmentmentioning

confidence: 99%

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

2021

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“…New optimization algorithms are being used more and more often. This group of algorithms consists of: the Ant Colony Optimization (ACO) algorithm [26], the Cuckoo Search (CS) algorithm [5,37], the Bat Algorithm (BA) [4] and the Gray Wolf Optimizer (GWO) algorithm [14]. Optimization calculations are often performed on simplified models (analytical or lumped parameters) of the PM motors [14,26,27].…”

Section: Introductionmentioning

confidence: 99%

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2021

EiN

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This paper presents the algorithm and computer software for constrained optimization based on the gray wolf algorithm. The gray wolf algorithm was combined with the external penalty function approach. The optimization procedure was developed using Borland Delphi 7.0. The developed procedure was then applied to design of a line-start PM synchronous motor. The motor was described by three design variables which determine the rotor structure. The multiplicative compromise function consisted of three maintenance parameters of designed motor and one non-linear constraint function was proposed. Next, the result obtained for the developed procedure (together with the gray wolf algorithm) was compared with results obtained using: (a) the particle swarm optimization algorithm, (b) the bat algorithm and (c) the genetic algorithm. The developed optimization algorithm is characterized by good convergence, robustness and reliability. Selected results of the computer simulation are presented and discussed.

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“…GAs and particle swarm optimization are very often used to solve optimization task with constraints (Kou et al , 2009; Wojciechowski, 2016). The application of new optimization methods, such as bat algorithm, artificial bee colony algorithm and cuckoo search algorithm, to solve constrained optimization problems is increasingly observed (Akay and Karaboga, 2017; Gandomi et al , 2013; Majumader and Mallick, 2016; Cvetkovski et al , 2018). The constraints are usually included by using the penalty function method.…”

Section: Introductionmentioning

confidence: 99%

Adaptation of the penalty function method to genetic algorithm in electromagnetic devices designing

Knypiński

2019

COMPEL

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Purpose The purpose of this paper is to elaborate the effective method of adaptation of the external penalty function to the genetic algorithm. Design/methodology/approach In the case of solving the optimization tasks with constraints using the external penalty function, the penalty term has a larger value than the primary objective function. The sigmoidal transformation is introduced to solve this problem. A new method of determining the value of the penalty coefficient in subsequent iterations associated with the changing penalty has been proposed. The proposed approach has been applied to the optimization of an electromagnetic linear actuator, and the mathematical model of the devices contains equations of the magnetic field, by taking into account the nonlinearity of ferromagnetic material. Findings The proposed new approach of the penalty function method consists in the reduction of the external penalty function in successive penalty iterations instead of its increase as it is in the classical method. In addition, the method of normalization of constraints during the formulation of optimization problem has a significant impact on the obtained results of optimization calculations. Originality/value The proposed approach can be applied to solve constrained optimization tasks in designing of electromagnetic devices.

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Cuckoo search as a tool for optimal design of PM brushless DC motor

Cited by 6 publications

References 15 publications

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

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Adaptation of the penalty function method to genetic algorithm in electromagnetic devices designing

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