Pareto set and EMOA behavior for simple multimodal multiobjective functions

Abstract

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem, and detect interesting interactions between induced changes to the Pareto-set and the ability of three optimization algorithms to keep track of Pareto-fronts.