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

Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …

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 …

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

Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

The development of the Internet of Vehicles (IoV) has made transportation systems into
intelligent networks. However, with the increase in vehicles, an increasing number of data …

This article proposes an end-to-end framework for solving multiobjective optimization
problems (MOPs) using deep reinforcement learning (DRL), that we call DRL-based …

There exist many multiobjective optimization problems (MOPs) containing a large number of
decision variables in real-world applications, which are known as large-scale MOPs. Due to …

In the last two decades, a variety of different types of multiobjective optimization problems
(MOPs) have been extensively investigated in the evolutionary computation community …

During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs)
have been proposed in the literature. As pointed out in some recent studies, however, the …