Many optimization problems have multiple objectives and these objectives are typically conflicting in nature. Because of the conflicting nature, there typically does not exist a single optimal solution but, rather, several so-called Pareto optimal (PO) solutions. The set of all Pareto optimal solutions in the objective space is called a Pareto front. To gain the greatest insight in to the problem it is necessary to consider all relevant objectives, which easily leads to many-objective optimization problems, (i.e. more than three objectives).This article introduces, and argues the advantages of using, interactive multi-objective optimization, especially the synchronous NIMBUS method for handling many-objective optimization problems as against other widely-used evolutionary multi-objective optimization algorithms