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 …

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

This paper describes jMetalPy, an object-oriented Python-based framework for multi-
objective optimization with metaheuristic techniques. Building upon our experiences with the …

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

Recently, more and more multi/many-objective algorithms have been proposed. However,
most evolutionary algorithms only focus on solving small-scale multi/many-objective …

In many-objective optimization problems (MaOPs), forming sound tradeoffs between
convergence and diversity for the environmental selection of evolutionary algorithms is a …

Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in
finding a representative set of Pareto optimal solutions in the past decade and beyond …