||The aim of multi-objective optimization is to obtain a set of (Pareto-)optimal solutions with different trade-offs between the objective functions, known as an approximation set. Especially in real-world optimization, it is often unknown which trade-off is desired beforehand. By presenting the decision maker with multiple solutions, insight is gained in the problem at hand, which can assist in selection of the final solution from the approximation set. However, when the problem at hand is multi-modal, the solutions in the approximation set might spread out over different modes. Two solutions that have similar objective values then might have completely different parameters. This makes navigation of the approximation set by the decision maker difficult and less insightful. To improve navigability, we aim in this project to enforce a form of smoothness, or continuity, between the parameters of different solutions in the approximation set.