Metamodels evolve even more frequently than programming languages. This evolution process may result in a large number of instance models that are no longer conforming to the revised metamodel. On the one hand, the manual adaptation of models after the metamodels’ evolution can be tedious, error-prone, and timeconsuming. On the other hand, the automated co-evolution of metamodels/models is challenging, especially when new semantics is introduced to the metamodels. While some interactive techniques have been proposed, designers still need to explore a large number of possible revised models, which makes the interaction timeconsuming. In this paper, we propose an interactive multi-objective approach that dynamically adapts and interactively suggests edit operations to designers based on three objectives: minimizing the deviation with the initial model, the number of non-conformities with the revised metamodel and the number of changes. The proposed approach takes their feedback into consideration by clustering the set of recommended co-evolution solutions of the multi-objective search. Thus, users can quickly select their preferred cluster and give feedback on a smaller number of solutions by eliminating similar ones. This feedback is then used to guide the search for the next iterations if the user is still not satisfied.We evaluated our approach on a set of metamodel/model co-evolution case studies and compared it to existing fully automated and interactive co-evolution techniques.