Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems

  • Heike Trautmann
  • Günter Rudolph
  • Christian Dominguez-Medina
  • Oliver Schütze
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

The averaged Hausdorff distance Δ p is a performance indicator in multi-objective evolutionary optimization which simultaneously takes into account proximity to the true Pareto front and uniform spread of solutions. Recently, the multi-objective evolutionary algorithm Δ p -EMOA was introduced which successfully generates evenly spaced Pareto front approximations for bi-objective problems by integrating an external archiving strategy into the SMS-EMOA based on Δ p . In this work a conceptual generalization of the Δ p -EMOA for higher objective space dimensions is presented and experimentally compared to state-of-the art EMOA as well as specialized EMOA variants on three-dimensional optimization problems.

Keywords

Pareto Front Multiobjective Optimization Multiobjective Optimization Problem Nondominated Solution Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Heike Trautmann
    • 1
  • Günter Rudolph
    • 1
  • Christian Dominguez-Medina
    • 2
  • Oliver Schütze
    • 3
  1. 1.Fakultät StatistikTechnische Universität DortmundDortmundGermany
  2. 2.Depto. de Ingeniería Eléctrica, Sección de ComputaciónCINVESTAV-IPNMexico CityMexico
  3. 3.Computer Science DepartmentCINVESTAV-IPNMexico CityMexico

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