A scalable multi-objective test problem toolkit

  • Authors:

  • Simon Huband;Luigi Barone;Lyndon While;Phil Hingston

  • Affiliations:

  • Edith Cowan University, Mount Lawley, WA, Australia;The University of Western Australia, Crawley, WA, Australia;The University of Western Australia, Crawley, WA, Australia;Edith Cowan University, Mount Lawley, WA, Australia

  • Venue:
  • EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
  • Year:
  • 2005

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Abstract

This paper presents a new toolkit for creating scalable multi-objective test problems. The WFG Toolkit is flexible, allowing characteristics such as bias, multi-modality, and non-separability to be incorporated and combined as desired. A wide variety of Pareto optimal geometries are also supported, including convex, concave, mixed convex/concave, linear, degenerate, and disconnected geometries. All problems created by the WFG Toolkit are well defined, are scalable with respect to both the number of objectives and the number of parameters, and have known Pareto optimal sets. Nine benchmark multi-objective problems are suggested, including one that is both multi-modal and non-separable, an important combination of characteristics that is lacking among existing (scalable) multi-objective problems.