Evaluating and comparing multi-objective optimizers is an important issue. But, when doing
a comparison, it has to be noted that the results can be influenced highly by the selected …

Evolutionary Algorithms have been applied successfully for solving real-world multi-
objective problems which explains the influx of newly proposed Multi-Objective Evolutionary …

Evolutionary algorithms have been successfully exploited to solve multiobjective
optimization problems. In the literature, a heuristic approach is often taken. For a chosen …

In recent years, the development of selection mechanisms based on performance indicators
has become an important trend in algorithmic design. Hereof, the hypervolume has been the …

Researchers have spent a considerable effort in evaluating the goodness of a solution set
obtained by an evolutionary multi-objective algorithm. However, most performance metrics …

Some quality indicators have been proposed for benchmarking preference-based
evolutionary multi-objective optimization algorithms using a reference point. Although a …

It may be generalized that all Evolutionary Algorithms (EA) draw their strength from two
sources: exploration and exploitation. Surprisingly, within the context of multiobjective (MO) …

The evaluation of multiobjective evolutionary algorithms (MOEAs) involves many metrics, it
can be considered as a multiple‐criteria decision making (MCDM) problem. A framework is …

The incorporation of performance indicators as the selection mechanism of a multi-objective
evolutionary algorithm (MOEA) is a topic that has attracted increasing interest in the last few …

The design of selection mechanisms based on quality assessment indicators has become
one of the main research topics in the development of Multi-Objective Evolutionary …