A scalable multi-objective test problem toolkit
Document Type
Conference Proceeding
Publisher
Springer Verlag
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Information Science
RAS ID
4425
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.
DOI
10.1007/978-3-540-31880-4_20
Comments
Huband, S., Barone, L., While, L., & Hingston, P. (2005, March). A scalable multi-objective test problem toolkit. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 280-295). Springer, Berlin, Heidelberg. Available here