Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a
decision maker with the guidance of his/her preferences which are provided progressively …

Evolutionary multi-objective optimization aims to provide a representative subset of the
Pareto front to decision makers. In practice, however, decision makers are usually interested …

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 …

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 …

Achieving balance between convergence and diversity is a key issue in evolutionary
multiobjective optimization. Most existing methodologies, which have demonstrated their …

Evolutionary algorithms (EAs) have shown to be promising in solving many-objective
optimization problems (MaOPs), where the performance of these algorithms heavily …

Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective
problems (MOPs) with more than three objectives. Their large numbers of objectives pose …

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and
efficiency in solving problems with two or three objectives. However, recent studies show …

Software systems nowadays are complex and difficult to maintain due to continuous
changes and bad design choices. To handle the complexity of systems, software products …

Evolutionary algorithms have been effectively used to solve multiobjective optimization
problems with a small number of objectives, two or three in general. However, when …