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 …

Differential evolution is a very popular optimization algorithm and considerable research has
been devoted to the development of efficient search operators. Motivated by the different …

An effective and efficient successful-parent-selecting framework is proposed to improve the
performance of differential evolution (DE) by providing an alternative for the selection of …

To reduce the effect of the selections of mutation strategies and control parameters on the
performance of differential evolution (DE), this paper proposes an improved differential …

Over the last few decades, a number of differential evolution (DE) algorithms have been
proposed with excellent performance on mathematical benchmarks. However, like any other …

Differential evolution (DE) has attracted much attention recently as an effective approach for
solving numerical optimization problems. However, the performance of DE is sensitive to the …

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 …

Differential evolution (DE) has been proven to be one of the most powerful global numerical
optimization algorithms in the evolutionary algorithm family. The core operator of DE is the …

Differential evolution (DE) is a remarkable evolutionary algorithm for global optimization
over continuous search space, whose performance is significantly influenced by its mutation …

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 …