No abstract

Differential Evolution (DE) is a powerful and versatile algorithmfor numerical optimization, but one of its downsides is its numberof parameters that need to be tuned. Multiple techniques have beenproposed to self-adapt DE’s parameters, with L-SHADE being oneof the most well established in the literature. This work presentsthe A-SHADE algorithm, which modifies the population size reductionschema of L-SHADE, and also EB-A-SHADE, which applies amutation strategy hybridization framework to A-SHADE. Thesealgorithms are applied to the CEC2013 benchmark set with 100dimensions, and it’s shown that A-SHADE and EB-A-SHADE canachieve competitive results.