Hindrances for robust multi-objective test problems

Seyedali Mirjalili, Andrew Lewis

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Abstract Despite the significant number of benchmark problems for evolutionary multi-objective optimisation algorithms, there are few in the field of robust multi-objective optimisation. This paper investigates the characteristics of the existing robust multi-objective test problems and identifies the current gaps in the literature. It is observed that the majority of the current test problems suffer from simplicity, so five hindrances are introduced to resolve this issue: bias towards non-robust regions, deceptive global non-robust fronts, multiple non-robust fronts (multi-modal search space), non-improving (flat) search spaces, and different shapes for both robust and non-robust Pareto optimal fronts. A set of 12 test functions are proposed by the combination of hindrances as challenging test beds for robust multi-objective algorithms. The paper also considers the comparison of five robust multi-objective algorithms on the proposed test problems. The results show that the proposed test functions are able to provide very challenging test beds for effectively comparing robust multi-objective optimisation algorithms.

Original languageEnglish
Article number2990
Pages (from-to)333-348
Number of pages16
JournalApplied Soft Computing Journal
Publication statusPublished - 18 Jul 2015
Externally publishedYes


  • Benchmark problem
  • Multi-objective optimisation
  • Multi-objective robust optimisation
  • Robust benchmark problem
  • Robust optimisation
  • Uncertainties


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