The paper describes different strategies for the design optimization of electromagnets. In order to compare the efficiency of these procedures, the same objective weighting function was minimized using a deterministic search algorithm, evolution strategy, and genetic algorithms. The objective function is based on minimizing the relative normal multipoles produced by the harmonic analysis of the flux density produced by the pole. The pole shape was treated differently in applying the various techniques to the problem. The optimization techniques were independently applied to optimize the magnet's shape. Results of the numerical solution as well as the harmonic analysis were compared. The methods are tested on an example of a superferric octupole magnet. Good results from the various techniques was achieved. A discussion on the practical aspects of these optimization methods is presented.
A new method for the design optimization of electromagnetic devices is presented. Improved Simulated Annealing (SA) is used along With the Hopfield neural network (€IN) to optimize a solution obtained from a trained multilayered perceptron (MLP). This results in an overall increase in the speed of optimization. Results are presented for two examples which show the effectiveness of the proposed method in obtaining a near optimal solution.In this paper, we present results obtained for a c-core magnetic circuit and a 2-pole dc motor using the proposed method. The simulated annealing algorithm has been modified by separating the search and state updating procedure, of the shortcomings of using with in design optimization problems. The results show the effectiveness of using the proposed method for design optimization.
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