Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which ...

Evolutionary algorithms (EAs) have shown to be efficient in dealing with many-objective optimization problems (MaOPs) due to their ability to obtain a set of compromising solutions which not only converge toward the Pareto ...

Among many-objective optimization problems (MaOPs), the proportion of nondominated solutions is too large to distinguish among different solutions, which is a great obstacle in the process of solving MaOPs. Thus, this paper ...

It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization ...

Convergence and diversity are two performance requirements that should be paid attention to in evolutionary algorithms. Most multiobjective evolutionary algorithms (MOEAs) try their best to maintain a balance between the ...

Evolutionary algorithms have shown their promise in addressing multiobjective problems (MOPs). However, the Pareto dominance used in multiobjective optimization loses its effectiveness when addressing many-objective problems ...