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 …

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 …

In evolutionary multiobjective optimization, maintaining a good balance between
convergence and diversity is particularly crucial to the performance of the evolutionary …

In numerous industrial applications where safety, efficiency, and reliability are among
primary concerns, condition-based maintenance (CBM) is often the most effective and …

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 …

While the industrial Internet of Things (IIoT) can support efficient control of the physical world
through large amounts of industrial data, data security has been a challenge due to various …

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for
computationally expensive optimization problems with more than three objectives. The …

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 …

Recently, a number of high performance many-objective evolutionary algorithms with
systematically generated weight vectors have been proposed in the literature. Those …