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The paper presents a comparison of performance of a number of selected optimization procedures when applied to solving electromagnetic field problems. The optimization techniques assessed encompass simulated annealing and genetic algorithms, as well as deterministic methods, including the Levenberg‐Marquardt procedure. The comparison is performed using a number of test functions followed by a study of two simple configurations relevant to problems encountered in electromagnetics.

The power transformer is one of the most important pieces of equipment in a power system. The necessity for the optimum design of a power transformer arises because the design chosen should satisfy all the limitations and restrictions placed on it. This paper presents an improved fast evolutionary programming (IFEP) technique for the optimal design of a three‐phase power transformer.

The optimization of the transformer design problem is formulated as an NLP problem, expressing the objective and constraint functions in terms of the selected independent variables. Here the cost of the transformer is considered as the objective function and is the sum of material cost of stampings and copper windings, cost of cooling tube arrangements, cost of cooling medium, insulation cost and labour cost. A computer program is written from which the optimal design parameters are obtained. For optimization, the classical evolutionary programming (CEP) technique and its variant the IFEP technique are used and the results are compared.

The application of CEP and IFEP for transformer design has been demonstrated on two test cases. It has been observed that this IFEP outperforms the CEP in obtaining the optimum design of transformers of smaller as well as larger ratings in terms of execution time, convergence rate, quality and success rate.

The proposed method results in the economical design of a three‐phase power transformer which can significantly reduce the cost of manufacturing transformers.