Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search

In this paper, we elaborate how decision space diversity can be integrated into indicator-based multiobjective search. We introduce DIOP, the diversity integrating multiobjective optimizer, which concurrently optimizes two set-based diversity measures, one in decision space and the other in objective space. We introduce a possibility to improve the diversity of a solution set, where the minimum proximity of these solutions to the Pareto-front is user-defined. Experiments show that DIOP is able to optimize both diversity measures and that the decision space diversity can indeed be improved if the required maximum distance of the solutions to the front is relaxed.