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 …

Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …

Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …

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 …

In pedagogy, teachers usually separate mixed-level students into different levels, treat them
differently and teach them in accordance with their cognitive and learning abilities. Inspired …

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 …

Large-scale optimization problems (LSOPs) are challenging because the algorithm is
difficult in balancing too many dimensions and in escaping from trapped bottleneck …