For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted
selection criteria based on Pareto dominance. However, the performance of Pareto-based …

In this paper, we propose the use of modified distance calculation in generational distance
(GD) and inverted generational distance (IGD). These performance indicators evaluate the …

In multiobjective optimization, a scalable test problem is one that can be formulated for an
arbitrary number of objectives. Scalable test problems evaluate the conceptual foundations …

Recently, increasing works have been proposed to drive evolutionary algorithms using
machine-learning models. Usually, the performance of such model-based evolutionary …

In the last decade, several evolutionary algorithms have been proposed in the literature for
solving multi-and many-objective optimization problems. The performance of such …

The Pareto front (PF) estimation has become an emerging strategy for solving multiobjective
optimization problems in recent studies. By approximating the geometrical structure of the …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

A key idea in many-objective optimization is to approximate the optimal Pareto front using a
set of representative non-dominated solutions. The produced solution set should be close to …

A real-world multiobjective optimization problem (MOP) usually has differently scaled
objectives. Objective space normalization has been widely used in multiobjective …

The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for
solving complex optimization problems. Since the operators (eg crossover, mutation …