When attempting to better understand the strengths and weaknesses of an algorithm, it is
important to have a strong understanding of the problem at hand. This is true for the field of …

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 …

The interests in multiobjective and many-objective optimization have been rapidly
increasing in the evolutionary computation community. However, most studies on …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

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 …

Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the
performance of evolutionary algorithms has not yet attracted much attention. This paper …

The Hausdorff distance d H is a widely used tool to measure the distance between different
objects in several research fields. Possible reasons for this might be that it is a natural …

This paper concerns multiobjective optimization in scenarios where each solution evaluation
is financially and/or temporally expensive. We make use of nine relatively low-dimensional …

I. INTRODUCTION Due largely to the nature of multiobjective evolutionary algorithms
(MOEAs), their behaviors and performances are mainly studied experimentally. In the past …

This paper proposes a multimodal multiobjective Differential Evolution optimization
algorithm (MMODE). The technique is conceived for deployment on problems with a Pareto …