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 …

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 …

DE algorithms have outstanding performance in solving complex problems. However, they
also have highlighted the need for an effective approach to alleviating the risk of premature …

Solving single objective real-parameter optimization problems, also known as a bound-
constrained optimization, is still a challenging task. We can find such problems in …

In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …

Proposing new mutation strategies to improve the optimization performance of differential
evolution (DE) is an important research study. Therefore, the main contribution of this paper …

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) is one of the highly acknowledged population-based optimization
algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems …

The performance of most metaheuristic algorithms depends on parameters whose settings
essentially serve as a key function in determining the quality of the solution and the …

Abstract Population size of Differential Evolution (DE) algorithms is often specified by user
and remains fixed during run. During the first decade since the introduction of DE the …