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This paper describes a new method aiming at the multi-objective optimization of a transverse flux machine with a claw-pole and flux-concentrating structure (CPTFM). Considering the 3-D main magnetic flux in the CPTFM, the time-consuming 3-D finite-element method (FEM) should be used to accurately analyze the performance of the machine. In order to reduce the calculation time for the optimization of the complex CPTFM, the Taguchi method is first applied to evaluate the importance of all the design parameters in the consideration of the predefined five objectives. Subsequently, a mathematical model is developed to replace the 3-D FEM, so that the objectives depending on the selected four critical parameters can be analytically calculated. With its help, the four parameters are further optimized with the multi-objective particle swarm optimization method and nondominated sorting genetic algorithm 2. Finally, the optimal design of the CPTFM is selected from all the searched Pareto solutions with the help of fuzzy logic. It can be concluded that the developed method is effective and efficient, which can make an appropriate compromise between the calculation time and the improvement of the machine performance. [ABSTRACT FROM PUBLISHER]

Using the finite-element model (FEM) to achieve the optimal design of electromagnetic devices is a complex and time-consuming process. Space-mapping techniques reduce computation cost by aligning two models with different granularities, namely, a coarse model and a fine model. In this paper, Kriging output space mapping (OSM), an original space-mapping technique based on adaptive nonlinear corrective projection, is proposed and compared with a conventional OSM technique. The device for optimal sizing is a five-phase linear induction motor, which is represented through two modeling levels: 1) coarse (Kriging model) and 2) fine (2-D FEM). An advantage of this new technique is its ability to provide a sufficiently accurate model for each objective and constraint function and make the coarse model converge effectively toward the fine model. [ABSTRACT FROM AUTHOR]

The surrogate-based optimization is largely used in engineering design to find optimal performance characteristics of computational expensive simulations such as the finite element method. An original multiple surrogate-based optimization criterion is proposed. The approach was tested on the Zakharov benchmark function and on the Loney solenoid benchmark problem. Simulation results and comparisons with some traditional Kriging-based global optimization methods show that the proposed method can be used very effectively in electromagnetic design optimization. [ABSTRACT FROM PUBLISHER]