Multimodal Optimization (MMO) aims at identifying several best solutions to a problem whereas classical optimization converge oftenly to only one good solution. MMO has been an active research area in the past years and several new evolutionary algorithms have been developed to tackle multimodal problems. In this work, we compare extensively three recent evolutionary algorithms (MoBiDE, Multimodal NSGAII and MOMMOP). Each algorithm uses multiobjectivization, together with niching techniques to address scalar (single objective) MMO problems. We have fully re-implemented MoBiDE and MM-NSGAII in order to better evaluate their sensitivity to parameter changes and their strengths and weaknesses. We have carefully evaluated all algorithms on the same benchmark functions and with the same parameters settings. The influence of the intrinsic parameters for each algorithm are stressed and the algorithms are also compared to a non-multimodal evolutionary algorithm to better highlight the impact of the multimodal adaptations. Moreover, full access to the detailed results and source code is granted on our website for the ease of reproducibility.