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The purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements are applicable to both single and multiobjective problems. Their combined use allows the optimization of complex 3D electromagnetic devices.

DE is improved by a combination of techniques which are applied in a cascade way and their single and combined effect is tested on well‐known benchmarks and domain‐specific applications.

It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems.

The increased speed of convergence may come at the price of a somewhat decreased robustness. However, such behavior is justified by the CPU time constraints under which the optimization has to be performed.

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

This paper explorers the combined use of many of the most recent and successful algorithmic improvements to DE and applies them to both single and multiobjective problems.