Linear scalarization for Pareto front identification in stochastic environments

M.M. Drugan

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Multi-objective multi-armed bandits (MOMAB) is a multiarm bandit variant that uses stochastic reward vectors. In this paper, we propose three MOMAB algorithms. The first algorithm uses a fixed set of linear scalarization functions to identify the Pareto front. Two topological approaches identify thePareto front using linearweighted combinations of reward vectors. The weight hyper-rectangle decomposition algorithm explores a convex shape Pareto front by grouping scalarization functions that optimise the same arm intoweight hyperrectangles. It is generally acknowledged that linear scalarization is not able to identify all the Pareto front for non-convex shapes. The hierarchical PAC algorithm iteratively decomposes the Pareto front into a set of convex shapes to identify the entire Pareto front. We compare the performance of these algorithms on a bi-objective stochastic environment inspired from a real life control application.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
Subtitle of host publication8th International Conference, EMO 2015, Guimarães, Portugal, March 29 - April 1, 2015. Proceedings, Part II
EditorsA. Gaspar-Cunha , C. Henggeler Antunes , C. Coello Coello
Place of PublicationBerlin
Number of pages16
ISBN (Electronic)978-3-319-15892-1
ISBN (Print)9783319158914
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2015) - Guimarães, Portugal
Duration: 29 Mar 20151 Apr 2015
Conference number: 8

Publication series

NameLecture Notes in Computer Science
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference8th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2015)
Abbreviated titleEMO 2015


  • Multi-objective multi-armed bandits
  • Pareto front identification
  • Scalarization functions
  • Topological decomposition


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