Scalability of optimization algorithms is a major challenge in coping with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …

Metaheuristic algorithms are extensively recognized as effective approaches for solving high-
dimensional optimization problems. These algorithms provide effective tools with important …

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong
in 1994 and since then many CCEAs have been proposed and successfully applied to …

Identification of variable interaction is essential for an efficient implementation of a divide-
and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an …

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 …

Cooperative coevolution (CC) algorithms based on variable decomposition methods are
efficient in solving large-scale optimization problems (LSOPs). However, many …

In this paper we use a divide-and-conquer approach to tackle large-scale optimization
problems with overlapping components. Decomposition for an overlapping problem is …

Through introducing the divide-and-conquer strategy, cooperative co-evolution (CC) has
been successfully employed by many evolutionary algorithms (EAs) to solve large-scale …

Cooperative coevolution (CC) provides a powerful divide-and-conquer architecture for large
scale global optimization (LSGO). However, its performance relies highly on decomposition …

Most real‐world optimization problems tackle a large number of decision variables, known
as Large‐Scale Global Optimization (LSGO) problems. In general, the metaheuristic …