Most existing dominance relations give higher priority to convergence than diversity and
cannot offer reasonable selection pressure according to the evolution status. This easily …

When solving multi-objective optimization problems (MOPs) with irregular Pareto fronts (eg,
disconnected, degenerated, inverted) via evolutionary algorithms, a critical issue is how to …

Many-objective optimization problems appear in a large many practical cases, but
maintaining the convergence and diversity of solutions becomes a big challenge. In …

Many-objective optimization problems (MaOPs) have attracted wide attention. However,
most solving methods prioritize the convergence or take the convergence and diversity into …

Most existing multi-objective evolutionary algorithms relying on fixed reference vectors
originating from an ideal or a nadir point may fail to perform well on multi-and many …

Recently, a number of resource allocation strategies have been proposed for evolutionary
algorithms to efficiently tackle multiobjective optimization problems (MOPs). However, these …

Large-scale many-objective optimization problems (LSMaOPs) pose great difficulties for
traditional evolutionary algorithms due to their slow search for Pareto-optimal solutions in …

Pareto dominance-based many-objective evolutionary algorithms (MaOEAs) face a
significant challenge from many-objective problems (MaOPs). The selection pressure …

In the realm of many-objective optimization, environmental selection based on Pareto-
dominance relations often yields a few dominance-resistant individuals (DRIs), which are …

Many-objective evolutionary algorithms have gained significant achievements over the
years. However, the difficulty in balancing convergence and diversity of the population …