Differential evolution (DE) algorithm has been shown to be a very effective and efficient
approach for solving global numerical optimization problems, which attracts a great attention …

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 …

Differential Evolution (DE) is a simple but powerful population-based stochastic optimization
algorithm. Owing to its simplicity, easy implementation and excellent performance, DE has …

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 …

The differential evolution (DE) algorithm is a notably powerful evolutionary algorithm that
has been applied in many areas. Therefore, the question of how to improve the algorithm's …

Differential evolution (DE) has been recognized as one of the most effective algorithms for
solving numerical optimization problems. In this paper, we propose a tri-population …

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

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 …

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 …

Generally, the optimization problem has different relationships (ie, linear, approximately
linear, non-linear, or highly non-linear) with different optimized variables. The choices of …