  Multimodal multi-objective problems (MMOPs) commonly arise in real-world
problems where distant solutions in decision space correspond to very similar
objective values. To obtain all solutions for MMOPs, many multimodal
multi-objective evolutionary algorithms (MMEAs) have been proposed. For now,
few studies have encompassed most of the recently proposed representative MMEAs
and made a comparative comparison. In this study, we first review the related
works during the last two decades. Then, we choose 12 state-of-the-art
algorithms that utilize different diversity-maintaining techniques and compared
their performance on existing test suites. Experimental results indicate the
strengths and weaknesses of different techniques on different types of MMOPs,
thus providing guidance on how to select/design MMEAs in specific scenarios.
