Abstract The Multiple Traveling Salesman Problem (MTSP) is among the most interesting
combinatorial optimization problems because it is widely adopted in real-life applications …

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 …

The job-shop scheduling problem (JSP) is NP hard, which has very important practical
significance. Because of many uncontrollable factors, such as machine delay or human …

The next revolution of the smart industry relies on the emergence of the Industrial Internet of
Things (IoT) and 5G/6G technology. The properties of such sophisticated communication …

Inverted generational distance (IGD) has been widely considered as a reliable performance
indicator to concurrently quantify the convergence and diversity of multiobjective and many …

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

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

Many-objective optimization has posed a great challenge to the classical Pareto dominance-
based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary …

A multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a classic
decomposition-based multi-objective optimization algorithm. In the standard MOEA/D …

Hybrid flow shop scheduling problems have gained an increasing attention in recent years
because of its wide applications in real-world production systems. Most of the prior studies …