Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

The job-shop scheduling problem (JSP) is NP hard, which has very important practical
significance. Because of many uncontrollable factors, such as machine delay or human …

Meta-heuristic algorithms are becoming more prevalent and have been widely applied in
various fields. There are numerous reasons for the success of such techniques in both …

Differential evolution (DE) is a population-based metaheuristic search algorithm that
optimizes a problem by iteratively improving a candidate solution based on an evolutionary …

Differential Evolution (DE) variants have been proven to be excellent algorithms in tackling
real-parameter single objective numerical optimization because they have secured the front …

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter
optimization algorithms in current use. DE operates through similar computational steps as …

Trial vector generation strategies and control parameters have a significant influence on the
performance of differential evolution (DE). This paper studies whether the performance of …

A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization
performance by implementing a new mutation strategy ldquoDE/current-to-p bestrdquo with …

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 …