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

This paper presents a state-of-the-art review on multi-objective metaheuristics for multi-
objective discrete optimization problems (MODOPs). The relevant literature source and their …

Feature selection (FS) is an important data processing technique in the field of machine
learning. There have been various FS methods, but all assume that the cost associated with …

This study introduces the extension of currently developed Seagull Optimization Algorithm
(SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull …

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

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

Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

The current literature of evolutionary many-objective optimization is merely focused on the
scalability to the number of objectives, while little work has considered the scalability to the …

Multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which
multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front …

This paper proposes an improved epsilon constraint-handling mechanism and combines it
with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve …