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 …

This article proposes an adaptive localized decision variable analysis approach under the
decomposition-based framework to solve the large-scale multiobjective and many-objective …

In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-
intensive tasks to edge server (ES) that provides additional computation resources. Due to …

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 …

Offspring generation plays an important role in evolutionary multiobjective optimization.
However, generating promising candidate solutions effectively in high-dimensional spaces …

It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto
front in large-scale multiobjective optimization. To tackle this problem, this article proposes a …

The generation and updating of solutions, eg, crossover and mutation, of many existing
evolutionary algorithms directly operate on decision variables. The operators are very time …

In real-world applications, there exist a lot of multiobjective optimization problems whose
Pareto-optimal solutions are sparse, that is, most variables of these solutions are 0 …

Recommender systems are of great significance for mining the data generated by the
Internet of Things (IoT) and are important for the intelligent IoT systems. The traditional …