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Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper. [ABSTRACT FROM PUBLISHER]

A topology optimization (TO) of synchronous motors based on the ON/OFF method to obtain practical shapes is presented in this paper. The TO using evolutionary algorithms such as genetic algorithm often yields complex shapes that are impossible to produce in an engineering sense. In the previously proposed method, lattice meshes were required for the ON/OFF method. This results in poor ability of expression for curved surfaces and restriction of mesh generation. To solve this problem, the algorithm is extended to adapt to unstructured meshes. The present method is applied to a shape optimization of synchronous motors, and optimization results show that feasible shapes are obtained and torque property is improved. [ABSTRACT FROM AUTHOR]

This paper proposes a surrogate-assisted multiobjective evolutionary algorithm based on decomposition (sMOEA/D) for the design of electric motors. The idea is to improve the surrogate gradually during the optimization. Simulation results show that the proposed method is competitive with state-of-the-art multiobjective optimization algorithms needing only a small number of function evaluations. [ABSTRACT FROM AUTHOR]

In the framework of nondestructive testing and evaluation, Lorentz force evaluation (LFE) is a method for reconstructing defects in electrically conducting laminated composites. In this paper, we propose a new inverse calculation strategy for LFE based on a stochastic optimization, the differential evolution (DE) algorithm. We determined the optimal control parameters for the DE and assessed its performance based on simulated and measured data. The results show that the depth of the defect was estimated correctly for all of the data sets that we evaluated. The geometry was reconstructed with errors of less than 4% relative to the size of the defect. The proposed scheme was robust against noise and distortions in the data measurements. We conclude that the proposed reconstruction scheme is a promising method for solving the inverse problem in LFE. [ABSTRACT FROM AUTHOR]

This paper compares two different identification methods of a static rate-independent energy-based hysteresis model with regard to the dynamic hysteresis loop shape prediction when coupled to the parametric magnetodynamic lamination model. The values of hysteresis model parameters are determined solely based on the quasi-static major loop. A semiphysical approach identifying the reversible and irreversible field components independently and a purely mathematical scheme using a differential evolution optimization algorithm determining all parameters simultaneously are compared. Both variants of parameter identification are analyzed in terms of hysteresis loop shape prediction for quasi-static as well as dynamic loops. [ABSTRACT FROM AUTHOR]

Multiple-objective designs exist in most real-world engineering problems in different disciplines. A multi-objective evolutionary algorithm will face a challenge to obtain a series of compromises of different objectives, called Pareto optimal solutions, and to distribute them uniformly. In this regard, it is essential to keep the balance of local and global search abilities of such algorithms. Quantum-behaved particle swarm optimization (QPSO) is a population-based swarm intelligence algorithm, and differential evolutionary (DE) is another simple population-based stochastic search one for global optimization with real-valued parameters. Although the two optimizers have been successfully employed to solve a wide range of design problems, they also suffer from premature convergence and insufficient diversity in the later searching stages. This is probably due to the insufficient dimensional searching strength, especially for problems with many decision parameters. In this paper, a new multi-objective non-dominated optimal methodology combining QPSO, DE, and tabu search algorithm (QPSO-DET) is proposed to guarantee the balance between the local and global searches. The performances of the proposed QPSO-DET are compared with those of other two widely recognized vector optimizers using different case studies. [ABSTRACT FROM AUTHOR]

Harmony search (HS) algorithm is an evolutionary optimization algorithm developed in an analogy with an improvisation process where musicians try to polish their pitches to obtain a better harmony. In this paper, a modified HS (MHS) algorithm is adapted to multi-objective optimization using external archiving, ranking with crowding distance, and control parameters tuning based on Ricker map to solve a transformer design optimization (TDO) problem with two competing objectives. Simulations applied to a TDO problem demonstrate the effectiveness of the proposed multi-objective MHS algorithm. Results indicate that, compared with other multi-objective HS algorithm, in terms of output quality, the proposed MHS is able to find competitive solutions with a good tradeoff between the design objectives. [ABSTRACT FROM PUBLISHER]

Design optimization, favoring efficiency maximization, is the subject of extended current research in small electric traction motor applications, aiming the increase in efficiency to be achieved without compromising power density. This paper proposes a mixed technique based on a particular multi-objective, population-based optimization methodology, utilizing a strength Pareto evolutionary algorithm variant combined with the finite element method and analytical tools. The resultant motor design has been validated through measurements on a prototype in-wheel permanent magnet motor for a light electric vehicle. [ABSTRACT FROM PUBLISHER]

The design of a transformer must meet minimum requirements of efficiency and regulation, maximizing the power to be transferred per unit of mass or volume, and supports a well defined maximum elevation of temperature. Conflicting objectives lead to the employment of optimization methods in the transformer design. Multiobjective optimization problems (MOPs) consist of several competing and incommensurable objective functions. Recently, as a search and optimization technique inspired by nature, evolutionary algorithms (EAs) have been broadly applied to solve MOPs. Various EAs have been proposed for this purpose, and their usefulness has been demonstrated in several application domains of science and engineering. In this paper, we propose the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) approach combined with chaotic sequences (CMOA). Our approach integrates the merits of both UPS-EMOA and chaotic sequences to improve the efficiency of the optimization procedure. Numerical results of transformer design optimization demonstrate the effectiveness of the proposed CMOA when compared with the UPS-EMOA approach to preserve the diversity of the solutions and find nondominated solutions. [ABSTRACT FROM AUTHOR]

In this paper, we present a Pareto classifier for a multiobjective evolutionary algorithm used to optimize the numerical model of an electrostimulative acetabular revision system. This classifier is based on the hyper-radial visualization method and enables us to automatically choose the most efficient stimulation electrode arrangement from a Pareto amount of optimal solutions to treat the pelvic bone after a hip revision surgery. It is based on two criteria: 1) the average performance concerning all optimization goals and 2) the general strength of tradeoffs in each goal function to achieve this performance. [ABSTRACT FROM AUTHOR]

To explore the full potential of Quantum-inspired Evolutionary Algorithms (QEA) in multiobjective design optimizations, a vector QEA is proposed. To fulfill the two ultimate goals of a vector optimizer in finding and uniformly sampling the Pareto front of a multi-objective inverse problem, a fitness assignment formula to consider the number of improvements in the whole objective functions and the amount of the improvement in a specified objective function, as well as the use of a selection mechanism in choosing the so far searched best solutions, are proposed in this paper. The information sharing and the increment angle updating components of the scalar QEA have also been redesigned according to the characteristics of multi-objective inverse problems. Numerical results on two case studies are presented to validate the proposed vector QEA. [ABSTRACT FROM AUTHOR]

Topology optimization using the binary information of magnetic material is one of the most attractive simulations for the conceptual design of electrical machines. Heuristic algorithms based on random search allow engineers to define general-purpose objects with various constraint conditions. However, it is difficult for topology optimization to realize a practical solution without island and void distribution. In this paper, we propose a hybrid evolutionary algorithm that is composed of a genetic algorithm (GA) and extended compact GA (ECGA). We verify the effectiveness of the proposed algorithm on the binary topology optimization problem for the design of magnetostatic shielding. [ABSTRACT FROM PUBLISHER]

The purpose of the paper is to demonstrate that the multicriteria formulation of an inverse problem fits well with current industrial electromagnetic design. The optimal shape design of a permanent-magnet (PM) generator is developed as the case study. Two well-known evolutionary algorithms, NSGA-II and MOESTRA, have been implemented and relevant results compared. [ABSTRACT FROM AUTHOR]

This paper presents a distributed universal evolutionary optimization environment designed for optimal design in electromagnetics. The optimizer can be tuned to act as a genetic algorithm or as an evolutionary strategy. Basic principles of optimizer design are discussed. The system is coded in Java and uses a remote method invocation technique to distribute computational tasks between local and/or remote servers. T.E.A.M Workshop problem 25 was used to estimate parallel performance of the system. An example of application is the optimal location of electrodes for electroconvulsive stimulation is presented. [ABSTRACT FROM AUTHOR]