Mathematics (Oct 2020)

A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems

  • Lourdes Uribe,
  • Johan M Bogoya,
  • Andrés Vargas,
  • Adriana Lara,
  • Günter Rudolph,
  • Oliver Schütze

DOI
https://doi.org/10.3390/math8101822
Journal volume & issue
Vol. 8, no. 10
p. 1822

Abstract

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Multi-objective optimization problems (MOPs) naturally arise in many applications. Since for such problems one can expect an entire set of optimal solutions, a common task in set based multi-objective optimization is to compute N solutions along the Pareto set/front of a given MOP. In this work, we propose and discuss the set based Newton methods for the performance indicators Generational Distance (GD), Inverted Generational Distance (IGD), and the averaged Hausdorff distance Δp for reference set problems for unconstrained MOPs. The methods hence directly utilize the set based scalarization problems that are induced by these indicators and manipulate all N candidate solutions in each iteration. We demonstrate the applicability of the methods on several benchmark problems, and also show how the reference set approach can be used in a bootstrap manner to compute Pareto front approximations in certain cases.

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