Differential evolution (DE) is one of the widely studied algorithms in evolutionary
computation. Recently, many adaptive mechanisms have been proposed for DE including …

Adaptive parameter control is critical in the design and application of evolutionary algorithm
(EA), so does in differential evolution. In the past decade, many adaptive evolutionary …

Many state-of-the-art evolutionary algorithms (EAs) can be categorized as sequential hybrid
EAs, in which various EAs are sequentially executed. The timing to switch from one EA to …

For most of differential evolution (DE) algorithm variants, premature convergence is still
challenging. The main reason is that the exploration and exploitation are highly coupled in …

Adaptive parameter control and mutation operator selection are two important research
avenues in differential evolution (DE). Existing works consider the two avenues …

The performance of an evolutionary algorithm (EA) is deeply affected by its control
parameter's setting. It has become a trend in recent studies to treat the control parameter as …

The intelligent optimization algorithm has the advantage of giving feasible solutions in
polynomial time when solving complex problems in reality. Its performance depends on its …

Extensive studies on selecting recombination operators adaptively, namely, adaptive
operator selection (AOS), during the search process of an evolutionary algorithm (EA), have …

Many multiobjective evolutionary algorithms (MOEAs) have been proposed for dealing with
various problem difficulties in multiobjective optimization over the past three decades …

Differential evolution (DE) is one of the most studied algorithms in evolutionary computation.
However, the parameters in DE need to be tuned carefully, which costs much computational …