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In systems optimization and machine learning multiple alternative solutions may exist in different parts of decision space for the same parts of the Pareto-front. The detection of equivalent Pareto-subsets may be desirable. In this paper we introduce a niching method that approximates Pareto-optimal solutions with diversity mechanisms in objective and decision space. For diversity in objective space we use rake selection, a selection method based on the distances to reference lines in objective space. For diversity in decision space we introduce a niching approach that uses the density based clustering method DBSCAN. The clustering process assigns the population to niches while the multi-objective optimization process concentrates on each niche independently. We introduce an indicator for the adaptive control of clustering processes, and extend rake selection by the concept of adaptive corner points. The niching method is experimentally validated on parameterized test function with the help of the S-metric.