Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

Multi-objective optimization problems are difficult to solve in that the optimized objectives are
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …

进化多目标优化主要研究如何利用进化计算方法求解多目标优化问题, 已经成为进化计算领域的
研究热点之一. 在简要总结2003 年以前的主要算法后, 着重对进化多目标优化的最新进展进行了 …

This article proposes an adaptive localized decision variable analysis approach under the
decomposition-based framework to solve the large-scale multiobjective and many-objective …

Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …

Hypervolume is widely used as a performance indicator in the field of evolutionary
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

Only a small number of function evaluations can be afforded in many real-world
multiobjective optimization problems (MOPs) where the function evaluations are …

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 …