NDLI: DBSCAN-based multi-objective niching to approximate equivalent pareto-subsets

Content Provider	ACM Digital Library
Author	Kramer, Oliver Danielsiek, Holger
Abstract	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.
Starting Page	503
Ending Page	510
Page Count	8
File Format	PDF
ISBN	9781450300728
DOI	10.1145/1830483.1830575
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Local search Memetic algorithms Hybrid metaheuristics Hybrid evolutionary multiobjective algorithm
Content Type	Text
Resource Type	Article

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DBSCAN-based multi-objective niching to approximate equivalent pareto-subsets