Applied Computing

In this paper, we revisit a general class of multimodal function optimizations using Evolutionary Algorithms (EAs) and, in particular, study a reformulation of multimodal optimization into a multiobjective framework. For both multimodal and multiobjective problems, most implementations need niching/sharing to promote diversity in order to obtain multiple (near-) optimal solutions. Such techniques work best when one has a priori knowledge of the problem for most real problems, however, this is not the case. In this paper, we solve multimodal optimizations reformulated into multiobjective problems using a steady-state multiobjective genetic algorithm which preserves diversity without niching. We find diverse solutions in objective space for two multimodal functions and compare these with previously published work. The algorithm without any explicit diversity-preserving operator is found to produce diverse sampling of the Pareto-front with significantly lower computational effort.