Metaheuristics are popularly used in various fields, and they have attracted much attention
in the scientific and industrial communities. In recent years, the number of new metaheuristic …

Differential evolution (DE) is one of the most efficient evolutionary algorithms for solving
numerical optimization problems; however, it still suffers from premature convergence and …

Evolutionary computation is attracting attention in the energy domain as an alternative to
tackle inherent mathematical complexity of some problems related to high-dimensionality …

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm
Optimization and Differential Evolution. Their initial versions were very simple, but rapidly …

Differential evolution (DE) has been experimentally demonstrated to be effective in solving
optimization problems. However, the effectiveness of DE encounters rapid deterioration in …

Various benchmark sets have already been proposed to facilitate comparison between
metaheuristics, or Evolutionary Algorithms. During the competition, typically algorithms are …

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 …

Mutation takes a vital part in assisting differential evolution (DE) to achieve satisfactory
performance. The most crucial factor for a good mutation scheme is to mutate individuals …

Differential evolution (DE) has shown remarkable performance in solving continuous
optimization problems. However, its optimization performance still encounters limitations …

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 …