Purpose – 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. Design/methodology/approach – 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. Findings – It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems. Research limitations/implications – 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. Practical implications – The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems. Originality/value – 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.

A Hybrid Multiobjective Differential Evolution Method for Electromagnetic Device Optimization

ALOTTO, PIERGIORGIO
2011

Abstract

Purpose – 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. Design/methodology/approach – 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. Findings – It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems. Research limitations/implications – 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. Practical implications – The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems. Originality/value – 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.
2011
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2481356
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