Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous
optimization. For these reasons DE has often been employed for solving various …

Differential evolution (DE) is among the most efficient evolutionary algorithms (EAs) for
global optimization and now widely applied to solve diverse real-world applications. As the …

To improve the optimization performance of LSHADE algorithm, an alternative adaptation
approach for the selection of control parameters is proposed. The proposed algorithm …

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 …

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 …

Seeking multiple optima simultaneously, which multimodal optimization aims at, has
attracted increasing attention but remains challenging. Taking advantage of ant colony …

Differential evolution (DE) is an efficient and powerful population-based stochastic search
technique for solving optimization problems over continuous space, which has been widely …

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with
nonlinear and complex problems. However, these population-based algorithms are …