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

We expose and contrast the impact of landscape characteristics on the performance of
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …

This work proposes a method to control the dominance area of solutions in order to induce
appropriate ranking of solutions for the problem at hand, enhance selection, and improve …

Diversity assessment of Pareto front approximations is an important issue in the stochastic
multiobjective optimization community. Most of the diversity indicators in the literature were …

In spite of the extensive studies that have been conducted regarding the construction of multi-
objective test problems, researchers have mainly focused their interests on designing …

The structure of the search space explains the behavior of multiobjective search algorithms,
and helps to design well-performing approaches. In this work, we analyze the properties of …

Studying the search behavior of evolutionary many-objective optimization is an important,
but challenging issue. Existing studies rely mainly on the use of performance indicators …

Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their
population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of …

The difficulty of solving a multi-objective optimization problem is impacted by the number of
objectives to be optimized. The presence of many objectives typically introduces a number …

In this paper, we show that when dealing with multi-objective combinatorial optimisation
problems, the search, in different executions of a multi-objective evolutionary algorithm …