Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …

In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective
functions and various constraints. Due to the presence of constraints, CMOPs' Pareto …

Convolutional neural networks (CNNs) have shown remarkable performance in various real-
world applications. Unfortunately, the promising performance of CNNs can be achieved only …

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

This article proposes utilizing a single deep reinforcement learning model to solve
combinatorial multiobjective optimization problems. We use the well-known multiobjective …

Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve
computationally expensive problems with some success. However, traditional EAs are not …

This article presents a new constraint-handling technique (CHT), called shift-based penalty
(ShiP), for solving constrained multiobjective optimization problems. In ShiP, infeasible …