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 …

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

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

In the last decades, a large number of metrics has been proposed to compare the
performance of different evolutionary approaches in multi-objective optimization. This …

For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted
selection criteria based on Pareto dominance. However, the performance of Pareto-based …

It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto
front in large-scale multiobjective optimization. To tackle this problem, this article proposes a …

The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make
use of aggregation functions to decompose a multiobjective optimization problem into …

Evolutionary multiobjective optimization has been a research area since the mid-1980s, and
has experienced a very significant activity in the last 20 years. However, and in spite of the …