Inspired by the evolutionary process in nature, Differential Evolution (DE) has been widely
concerned and used as a numerical global optimizer for decades of years, since its …

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical
optimization. However, the performance of DE significantly relies on its mutation operator …

Differential evolution (DE) is a simple yet effective algorithm for numerical optimization, and
its performance significantly depends on mutation operator and control parameters …

Differential evolution (DE) is an efficient global optimization algorithm. However, due to its
random properties, some individuals may mutate in the direction of deviating from the …

Differential Evolution (DE) was a powerful population-based evolutionary algorithm for
global optimization, and it achieved great success in both evolutionary computation …

The performance of differential evolution (DE) algorithm heavily depends on the
evolutionary scale, which is controlled by the generation operations including mutation …

Differential evolution is a famous and effective branch of evolutionary computation, which
aims at tackling complex optimization problems. There are two aspects significantly affecting …

Abstract The Differential Evolution (DE) algorithm is one of the most popular and studied
approaches in Evolutionary Computation (EC). Its simple but efficient design, such as its …

Differential evolution (DE) is one of the most popular and widely used optimizers among the
community of evolutionary computation. Despite numerous works having been conducted …

Exploration and exploitation are two cornerstones of evolutionary algorithms. An appropriate
balance between exploration and exploitation can drive a search process toward global …