Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

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 recent years, the development of new algorithms for multiobjective optimization has
considerably grown. A large number of performance indicators has been introduced to …

Complexity and variety of modern multiobjective optimisation problems result in the
emergence of numerous search techniques, from traditional mathematical programming to …

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 …

Offspring generation plays an important role in evolutionary multiobjective optimization.
However, generating promising candidate solutions effectively in high-dimensional spaces …

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 …

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 …

Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …