Constrained optimization of line-start PM motor based on the gray wolf optimizer

Abstract

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.

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

“…Therefore, for the PSO algorithm, the external penalty function should be applied. The external penalty must systematically increase in subsequent iterations [14]. In the case of the CS algorithm, a gentler way of considering constraints can be employed.…”

“…The calculations were repeated over ten runs for the same numbers of particles and a maximum number of iterations, as was the case for the CS algorithm. The following parameters of the PSO algorithm were applied: w = 0.2, c 1 = 0.35, and c 2 = 0.45 [14]. Additionally, the best, worst, and mean values of the objective functions and standard deviations for both algorithms were compared.…”

“…Nondeterministic algorithms are effective, and are often used to solve difficult problems arising in the synthesis of the electromagnetic transducers, where a solution is looked for within multidimensional sets of design variables [14]. Among these nondeterministic methods, the nature-inspired algorithms are popular and based on observations of phenomena in the natural environment.…”

“…A much smaller number of manuscripts have addressed the application of the CS algorithm when solving constrained optimization problems [18,19]. The external penalty function can be applied for most of the heuristic algorithms [1,9,11,14,15]. In the case of using the CS algorithm to solve constraint optimization problems, as in the case of other heuristic algorithms, scientists should select the method they use to take into account constraints very carefully.…”

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

“…Optimization algorithms are essential for numerous optimization applications where usually certain parameters are minimized or maximized by considering an objective function. Optimization algorithms can be classified as either deterministic or non-deterministic [1]. Deterministic algorithms are exact methods, and usually they need a substantial amount of time and resources for solving large optimization problems.…”