Search results

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 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 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 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 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.

The temperature drop, especially in the edge of rolled steel in the hot rolling cooling has a catastrophic effect on the steel quality. The purpose of this paper is to study the coupled eddy current-temperature field of a C-type edge induction heater to provide references for engineering applications and designs.

Three-dimensional finite element analysis (FEA) model of a C-type edge induction heater is developed. Especially, a numerical methodology to couple the eddy current and temperature fields is proposed for coupled eddy current and temperature problems involving movement components. FEA software ANSYS is used to solve the coupled eddy current and temperature fields. The heat loss from the eddy current fields is abstracted and processed, and taken as internal heat source in the analysis of the temperature field. The temperature distribution of the rolling steel is obtained.

The numerical results can predict exactly the temperature rise of the rolled steel by means of the edge induction heating system.

The proposed numerical methodology for coupling eddy current and temperature fields can be applied to engineering coupled eddy current and temperature problems involving movement components. Also, the developed model and method can be used in the analysis and design of the edge induction heating system.

A numerical methodology to couple eddy current and temperature field for solving multi-physics field problems involving movement components is proposed and implemented in available commercial software. A three-dimensional model of the C-type edge induction heat heater is developed. Finite element method is employed to study the coupled eddy current-thermal problem. A method to deal with the movement of the strip steel is proposed. The proposed methodology can be applied to other coupled eddy current-temperature field problem with moving components.

A new response surface model (RSM), the moving least squares (MLS) approximation, is proposed for reconstructing the objective/constraint functions for the design optimization of electromagnetic devices. The reconstructed functions are then combined with the simulated annealing (SA) algorithm to develop a computationally efficient technique to obtain the global solutions. The new method has: the “intelligence” to arrange the sample points, i.e. intensify the sample points in regions where a local optimum is likely to exist; the flexibility in dealing with irregular sample points; the self‐adaptive ability to regulate the parameters of the MLS models. Detailed numerical examples are given to validate the proposed technique.

The aim of this paper is to explore the potential of particle swarm optimization (PSO) methods for minimizing the sidelobe levels (SLL) and placing null at arbitrary angles of a nonlinear antenna array.

An improved PSO algorithm is designed.

The improved PSO method is an efficient and robust global optimizer for minimizing the SLL and placing null at arbitrary angles of a nonlinear antenna array.

Some improvements, such as the design of some new formulae for both position and velocity updating, the introduction of an age variable, and the devise of an intensification searches using the cross entropy method, are proposed.