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 …

This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …

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 …

Swarm intelligence is one of the most promising area for the researchers in the field of
numerical optimization. Researchers have developed many algorithms by simulating the …

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 …

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 …

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

This paper presents adaptive guided differential evolution algorithm (AGDE) for solving
global numerical optimization problems over continuous space. In order to utilize the …

After an introduction that includes a discussion of the classic random walk, this paper
presents a step-by-step development of the differential evolution (DE) global numerical …

Differential evolution (DE) is well known as a simple and efficient scheme for global
optimization over continuous spaces. It has reportedly outperformed a few evolutionary …