Another evolution of generalized differential evolution: variable number of dimensions

Abstract

The article proposes a multi-objective optimization method called Generalized Differential Evolution (GDE3) for a Variable Number of Dimensions (VND). The well-known generalized differential evolution is adapted to handle problems where the number of decision space variables is not a priori known. The performance of the method is assessed on a set of benchmark problems based on standard multi-objective test cases modified to depend on the number of decision space variables. Experimental results include a study of the setting of controlling parameters and a comparison with the state-of-the-art Variable-Length GDE3 (VLGDE3) algorithm. Finally, the novel approach is compared to the standard approach in a linear antenna array design problem.

Keywords:

• Multi-objective optimization
• evolutionary optimization
• variable number of dimensions
• generalized differential evolution

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research reported in this article was financed by the Czech Science Foundation [grant number 19-06049S]; Vysoké Učení Technické v Brně (Brno University of Technology) [grant number FEKT-S-20-6325].