In differential evolution (DE) studies, there are many parameter adaptation methods, aiming
at tuning the mutation factor F and the crossover probability CR. However, these methods …

The performance of differential evolution (DE) largely depends on its mutation strategy and
control parameters. In this paper, we propose an adaptive DE (ADE) algorithm with a new …

As the mutation strategy and algorithmic parameters in differential evolution (DE) are
sensitive to the problems being solved, a hot research topic is to adaptively control the …

Recently, various offspring generation strategies and parameter adaptation mechanisms
have been developed to enhance the reliability and robustness of differential evolution (DE) …

The main parameters that affect the performance of a differential evolution algorithm are the
scaling factor, crossover rate, and population size. To adaptively adjust the population …

Differential Evolution (DE) is one of the most effective approaches in Evolutionary
Computation to tackling complex black-box optimization, however, the overall performance …

Differential evolution (DE) is a well-known evolutionary algorithm with simple operation and
excellent performance, which has been applied to solve various optimization problems. To …

The performance of Differential Evolution (DE) algorithm is significantly affected by its
parameter setting. But the choice of parameters is heavily dependent on the problem …

Abstract Although the Differential Evolution (DE) algorithm has been shown to be a simple
yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced …

Differential evolution (DE) is a well-known optimization algorithm that utilizes the difference
of positions between individuals to perturb base vectors and thus generate new mutant …