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PurposeThe purpose of this paper is to present a parallel implementation of an evolution strategy (ES) algorithm for optimization of electromagnetic devices. It is intended for multi‐core processors and for optimization problems that have objective function representing a numerical simulation of electromagnetic devices. The speed‐up of the optimization is evaluated as a function of the number of processor cores used.Design/methodology/approachTwo parallelization approaches are implemented in the program developed – using multithreaded programming and using OpenMP. Their advantages and drawbacks are discussed. The program is tested on two examples for optimization of electromagnetic devices.FindingsUsing the developed parallel ES algorithm on a quad‐core processor, the optimization time can be reduced 2.4‐3 times, instead of the expected four times. This is due to a number of system processes and programs that run on part of the cores.Originality/valueA new parallel ES optimization algorithm has been developed and investigated. The paper could be useful for researchers aiming to diminish the optimization time by using parallel evolution optimization on multi‐core processors.

Purpose -The purpose of this paper is to consider the optimization of an 8-electrode cylindrical electrical capacitance tomography (ECT) sensor. The aim is to obtain maximum uniformity and value of the sensitivity distribution of the sensor, while keeping the mutual capacitances between the electrodes above a predefined level. Design/methodology/approach -The optimization methods that have been used are response surface methodology, genetic algorithm and a combination of both. Findings -As results, optimum dimensions for the gap, mounting pipe, shield and insulation are determined, which ensure more uniform distribution of sensitivity in the sensing area. Originality/value -The optimization strategies used -RSM and the combined RSM þ GA -make the optimization of ECT sensors feasible. The results show the effectiveness of the RSM þ GA strategy which could also be used for optimization of 3D multilayer ECT sensors.