Integrating decision space diversity into hypervolume-based multiobjective search

Abstract

Multiobjective optimization in general aims at learning about the problem at hand. Usually the focus lies on objective space properties such as the front shape and the distribution of optimal solutions. However, structural characteristics in the decision space can also provide valuable insights. In certain applications, it may even be more important to find a structurally diverse set of close-to-optimal solutions than to identify a set of optimal but structurally similar solutions. Accordingly, multiobjective optimizers are required that are capable of considering both the objective space quality of a Pareto-set approximation and its diversity in the decision space.