A Scalable Multi-objective Test Problem Toolkit

Luigi Barone, Lyndon While, S. Huband, P. Hingston

Research output: Contribution to journalArticlepeer-review

297 Citations (Scopus)


This paper presents a new toolkit for creating scalable multiobjective 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 multiobjective 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.
Original languageEnglish
Pages (from-to)280-295
JournalLecture Notes in Computer Science
Publication statusPublished - 2005


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