Multimodal Multiobjective Optimization (MMMOO) aims to detect and approximate the entire Pareto Set of a multiobjective optimization problem, even when a portion of it can represent the entire Pareto Front. It is a relatively recent field of research in evolutionary computation that combines the strengths of two well-established areas: multimodal optimization and multiobjective optimization. Like multiobjective optimization, it offers a set of solutions that highlight the best trade-offs among problem objectives. Like multimodal optimization, it provides the decision maker with distinct alternative solutions for a selected trade-off among objectives.

This research field has seen a growing number of new methods, typically formed by integrating a niching strategy into a multiobjective optimizer. However, special attention should be paid to reliable, comprehensive, and pragmatically sound performance assessment and comparison of these methods to guide this emerging field in the right direction. This competition aims to fulfill that goal by providing a fair and thorough experimental setup for evaluating and comparing MMMOO methods.