Title

A scalable multi-objective test problem toolkit

Document Type

Conference Proceeding

Publisher

Springer-Verlag

Faculty

Computing, Health and Science

School

School of Computer and Information Science

RAS ID

4425

Comments

Originally published as: 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. Original article available here

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

 
COinS
 

Link to publisher version (DOI)

10.1007/978-3-540-31880-4_20