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The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems.

A modified QPSO algorithm is designed.

The modified QPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic inverse problems. More specially, the experimental results as reported on different case studies demonstrate that the proposed method can find better final optimal solution at an early stage of the iterating process (uses less iterations) as compared to other tested optimal algorithms.

The modifications include the design of a new position updating formula, the introduction of a new mutation strategy and a dynamic control parameter to intensify the convergence speed of the algorithm.

The purpose of this work is to develop a computational paradigm for performance analysis of low-frequency electromagnetic devices containing both magnetic metamaterials (MTMs) and natural media.

A time domain finite element method (TDFEM) is proposed. The electromagnetic properties of the MTMs are modeled by a nonstandard Lorentz model. The time domain governing equation is derived by converting the one from the frequency domain into the time domain based on the Laplace transform and convolution. The backward difference is used for the temporal discretization. An auxiliary variable is introduced to derive the recursive formula.

The numerical results show good agreements between the time domain solutions and the frequency domain solutions. The error convergence trajectory of the proposed TDFEM conforms to the first-order accuracy.

To the best knowledge of the authors, the presented work is the first one focusing on TDFEMs for low-frequency near fields computations of MTMs. Consequently, the proposed TDFEM greatly benefits the future explorations and performance evaluations of MTM-based near field devices and systems in low-frequency electrical and electronic engineering.

The purpose of this paper is to develop a modeling method for the analysis of low-frequency metamaterials (MTMs) and their near-field applications.

The Euler–Lagrange method is introduced. An MTM is modeled as a multi-degree-of-freedom system without homogenization. The properties and the responses of the MTM in a near-field device are readily and rigorously studied through the motion equation derived from the Lagrange equations. The resonance frequencies and the corresponding resonance modes are solved from the characteristic equation.

The numerical results of the proposed method show good agreement with the experimental ones. A measurement of MTM-core coil resistance and inductance shows high accuracy of the proposed method.

The proposed Euler–Lagrange method provides a new study perspective and enables more flexible, rigorous and straightforward analysis of low-frequency MTMs in near-field applications. Consequently, the presented work greatly facilitates further explorations and studies on various novel MTM-based low-frequency near-field devices and systems.

The purpose of this paper is to develop a robust optimization methodology for metamaterial (MM) unit designs to minimize the effect of manufacturing and operational uncertainties.

A new robustness quantification function, applicable to both convex and nonconvex relationships between the mean and the standard deviation, is introduced. A distance-based local radial basis function network surrogate model is proposed to substitute the global radial basis function network to reduce the heavy computational cost without any scarification on the solution accuracy.

The optimized results of a prototype MM unit demonstrate the feasibility and merit of the proposed methodology. The proposed methodology outperforms the existing ones in both performance and robust parameters in the design of a prototype MM unit.

It provides a robust optimization methodology for MM units when considering the imperfections in fabrications and fluctuations in operation and environment conditions in engineering applications.

The aim of this paper is to explore the potential of standard quantum particle swarm optimization algorithms to solve single objective electromagnetic optimization problems.

A modified quantum particle swarm optimization (MQPSO) algorithm is designed.

The MQPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic design problems. The numerical results as reported have demonstrated that the proposed approach can find better final optimal solution at an initial stage of the iterating process as compared to other tested stochastic methods. It also demonstrates that the proposed method can produce better outcomes by using almost the same computation cost (number of iterations). Thus, the merits or advantages of the proposed MQPSO method in terms of both solution quality (objective function values) and convergence speed (number of iterations) are validated.

The improvements include the design of a new position updating formula, the introduction of a new selection method (tournament selection strategy) and the proposal of an updating parameter rule.

The purpose of this paper is to provide an accurate model and method to simulate the transient performances of an insulated gate bipolar transistor (IGBT) in an arbitrary free-carrier injection condition.

A numerical model and method for solving the physics-based model, an ambipolar diffusion equation-based model, of an IGBT is proposed.

The results of the proposed model are very close to the tested ones.

A mathematical model for an IGBT considering all free-carrier injection conditions is introduced, and a numerical solution methodology is proposed.

The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit.

A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA).

The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA.

A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.

The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem.

A modified PSO algorithm is designed.

The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm.

Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

The purpose of this paper is to explore the potential challenges in developing numerical methodologies for inverse problems and optimizations.

Summarizing previous research results mainly contributed by two research groups of Zhejiang University and Hong Kong Polytechnic University.

Computational intelligence plays an essential role in studying inverse problems and optimizations.

An up-to-date review on the current status of numerical methodologies, especially computational intelligences, for inverse problems and optimizations contributed by Chinese researchers.