Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

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 …

Generative adversarial networks (GANs) have been effective for learning generative models
for real-world data. However, accompanied with the generative tasks becoming more and …

Chaotic-based image encryption approaches have attracted great attention in the field of
information security. The properties of chaotic maps such as randomness and sensitivity …

Large-scale multi-objective optimization problems (MOPs) that involve a large number of
decision variables, have emerged from many real-world applications. While evolutionary …

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

In high dimensional environment, the interaction among particles significantly affects their
movements in searching the vast solution space and thus plays a vital role in assisting …

The uncertainty in electric power demand associated with the increasing penetration of
electric vehicles can profoundly affect the stability of microgrids. Therefore, the present work …

The performance of traditional multiobjective evolutionary algorithms (MOEAs) often
deteriorates rapidly as the number of decision variables increases. While some efforts were …

As one of the classical problems in the economic market, the newsvendor problem aims to
make maximal profit by determining the optimal order quantity of products. However, the …