Automated Aggregation and Omission of Objectives for Tackling Many-Objective Problems

Dimo Brockhoff () and Eckart Zitzler
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Dimo Brockhoff: Computer Engineering and Networks Laboratory, ETH Zurich

A chapter in New Developments in Multiple Objective and Goal Programming, 2010, pp 81-102 from Springer

Abstract: Abstract Many-objective problems pose various challenges in terms of decision making and search. One approach to tackle the resulting problems is the automatic reduction of the number of objectives such that the information loss is minimized. While in a previous work we have investigated the issue of omitting objectives, we here address the generalized problem of aggregating objectives using weighted sums. To this end, heuristics are presented that iteratively remove two objectives and replace them by a new objective representing an optimally weighted combination of them. As shown in the paper, the new reduction method can substantially reduce the information loss and thereby can be highly useful when analyzing trade-off sets after optimization as well as during search to reduce the computation overhead related to hypervolume-based fitness assignment.

Keywords: Original Objective; Greedy Heuristic; Objective Pair; Solution Pair; Dominance Structure (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-3-642-10354-4_6

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