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In this paper, a novel algorithm “firework optimization” (FO) is introduced, as well as its hybridization with generating set search (GSS). FO is an exploration algorithm, which intends to explore a vast region within the search space in order to determine regions that may contain a regional optimum. As the regions with potential are defined, GSS, the exploitation algorithm is applied to determine whether regions determined by FO contain a local or a global optimum. The validity of FO and GSS hybridization algorithm is verified using Branin and Six-Hump Camel test functions, and the hybridization algorithm is applied to an interior permanent magnet synchronous motor, in order to minimize torque ripple. [ABSTRACT FROM AUTHOR]

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, nonlinear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. The proposed algorithm interprets the problem as an unexplored terrain for climbing. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The efficiency of the proposed algorithm is demonstrated by application to conventional test functions. In this paper, the simulation results show that skewing does not necessarily reduce the cogging torque but may cause it to increase for certain pole-arc to pole-pitch ratio. The developed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one. [ABSTRACT FROM AUTHOR]

Automatic differentiation (AD) is introduced as a powerful technique to compute derivatives of functions given in the form of computer programs in high-level programming languages such as FORTRAN, C, or C++. This paper applies AD to compute error-free gradients of electromagnetic device sizing models. Then, the obtained gradients are exploited in optimization to size electromagnetic devices by means of minimizing a cost function with constrained parameters and performances. Often, the electromagnetic devices models have to be described not only by analytical formulas, but also by algorithms. This paper proposes an electromagnetic model of a linear actuator dealing with implicit equations solved by numerical algorithms. The ADOL-C package is considered for automatic differentiation. [ABSTRACT FROM PUBLISHER]

The design of an electromagnetic machine is a nonlinear, multivariable, and multimodal optimization problem that incurs a great deal of computation time when calculating electromagnetic fields. To overcome these problems effectively, this paper proposes a new evolutionary multimodal optimization algorithm based on the Big Bang–Big Crunch method and aided by a surrogate model using the theory of compressed sensing. Its efficiency is demonstrated by assessing the optimization results for test functions. Moreover, to evaluate the feasibility of its application to an electromagnetic problem, an interior permanent magnet motor is designed using the proposed algorithm. The obtained results confirm that the proposed method has the ability to search for multiple optimal solutions with a good computational efficiency by reducing the number of fitness function evaluations during the optimization process. [ABSTRACT FROM AUTHOR]

The conventional climb method is difficult to apply for the multimodal and multi-dimensional optimization problem. To solve this problem, the improved climb method is proposed in this paper. The proposed algorithm can mitigate the time for the optimization and increase the reliability for the optimization. The proposed algorithm is verified through the optimization of an axial flux permanent magnet motor. [ABSTRACT FROM PUBLISHER]

The uncertainties in design variables are unavoidable in the optimal design of electromagnetic devices, and there is an imperative demand to find a robust design, which is insensitive to the uncertainties and remains within the feasible region of constraints even perturbed by the uncertainties. In this paper, a gradient-based worst case optimization (G-WCO) algorithm is proposed in a limited uncertainty set to increase the numerical efficiency based on the worst case optimization (WCO) algorithm. Through applications to the robust optimal design of TEAM 22, the performances of the proposed G-WCO, conventional WCO, and multiobjective optimization approach using gradient index (GI) are compared. [ABSTRACT FROM AUTHOR]

Magnetic gears (MGs) are devices which operate through the interaction of magnetic fields produced by multipole magnets to transmit torque with high efficiency. Compared with mechanical gears, it requires no moving contact for the force transmission, hence there are no mechanical fatigue and no mechanical loss and less acoustic noise. There is no need for lubrication and hence MG requires minimal maintenance. However, the heavy use of permanent magnetic (PM) materials leads to a high production cost. In this paper, a novel mesh adjustable finite-element algorithm is proposed to optimize the magnetic gear dimensions in order to maximize the torque output for a given amount of PMs. With the proposed mesh adjustable finite-element algorithm, the coordinates of mesh nodes are moved according to dimensional changes, without compromising the mesh quality. The merit is that no re-mesh is required during the process of optimization, which can significantly reduce the computing time while retaining the robustness of the algorithm. By combining the proposed approach with particle swarm optimization (PSO) algorithm, a reliable convergence to the finding of global optimum is achieved. This proposed method is applied to optimize the dimensions of a coaxial magnetic gear with surface mounted PMs. Optimal results confirm the validity and effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Recently, a new kind of socio-politically motivated global search metaheuristic called imperialist competitive algorithm (ICA) was proposed. ICA is based on a form of imperialistic competition in which the populations are represented by countries divided among imperialists and colonies. In this paper, a modified ICA (MICA) approach based on concepts of attraction and repulsion between the colony and its imperialist is introduced during the search for better solutions. A brushless direct current wheel motor benchmark problem is used to investigate the performance of the classical ICA and the proposed MICA and results are shown to be competitive with those of other well-established optimization methods. [ABSTRACT FROM PUBLISHER]

In this paper, a planar wideband bandpass filter with complementary split-ring resonator (CSRR)—loaded transmission line is optimally designed taking into account a multi-regional objective function. Eight physical dimensions are set to be design parameters of the filter structure, and the Evolution Strategy (ES) algorithm is adopted to optimize the multi-parameter problem. The result shows that a simple gap capacitance model can make wideband bandpass filter and accomplish the design goal well with smaller size by the proposed optimization technique. [ABSTRACT FROM PUBLISHER]

Optimizing complex engineering problems may demand large computational efforts because of the use of numerical models. Global optimization can be established through the use of evolutionary algorithms, but may demand a prohibitive amount of computational time. In order to reduce the computational time, we incorporate in the global optimization procedures a physics-based fast coarse model. This paper presents a two-level genetic algorithm (2LGA) for electromagnetic optimization. This algorithm employs the global convergence properties of the genetic algorithm, where acceleration of the optimization results from the fast computations of the coarse model (low level) and where accuracy is guaranteed by using a limited number of fine model (high level) evaluations. Using the coarse model, we iteratively build surrogate models (intermediate levels) where metamodels produce surrogate models which approximate the fine model. The proposed algorithm comprises internal parameters which are self-tunable. We applied the 2LGA to the optimization of an algebraic test function, to the optimization of a die press model (TEAM Workshop Problem 25) and to the optimization of an octangular double-layered electromagnetic shield. The results show that the 2LGA is converging to the optimal solutions as the traditional genetic algorithm and that the acceleration is dependent on the accuracy of the low level. An acceleration factor of more than two can be achieved. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for computing formally sensitivities of the magnetic moment method. This numerical method is known to be very accurate and light to compute force and torque acting on an open field problem. We propose an approach which computes formal derivatives with respect to the geometrical parameters of the device. Once the sensitivities are obtained, gradient based optimization algorithm can be used. [ABSTRACT FROM AUTHOR]

Latin hypercube is a sampling technique for searching n dimensional space. Like Monte Carlo methods, it retains random qualities, and yet Latin hypercube is consistently more effective than Monte Carlo. Despite this fact, not a single paper has been published in IEEE TRANSACTIONS ON MAGNETICS on its use. Field analysis is a long way from delivering vectorized solutions where a vector of inputs can be processed. Stochastic algorithms are exceptionally inefficient compared to their deterministic counterparts. The best optimization tool would be a deterministic method which quickly and effectively interrogates the search space. Latin hypercube sampling, combined with pattern search solutions, comes close to achieving that objective. An improved solution for the magnetic TEAM Workshop problem 22 is presented using these tools. [ABSTRACT FROM AUTHOR]

This paper describes a fitting algorithm suitable for simultaneously approximating the real and imaginary parts of transformer admittance curves. The algorithm follows a unique strategy to determine the best initial guess. It optimizes the parameters one group at a time. This technique allows the fitting routine to find the best solution even when the number of optimization parameters is large. Since the optimization of each group of parameters is well controlled at each stage, the algorithm is suitable for constrained optimization. Another advantage is that the starting point for each stage can be very simple. Examples demonstrate that the algorithm produces good results for a variety of transformer admittance curves. [ABSTRACT FROM AUTHOR]