University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Cheng, R, Jin, Y, Olhofer, M and Sendhoff, B (2016) Test Problems for Large-Scale Multiobjective and Many-Objective Optimization IEEE Transactions on Cybernetics.

LSMOP_Final Sept 2016.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (728kB) | Preview


The interests in multi- and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multi- and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multi- and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multi- and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multi- and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and non-uniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multi- and many-objective evolutionary algorithms are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new evolutionary algorithms dedicated to large-scale multi- and many-objective optimization.

Item Type: Article
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Cheng, R
Jin, Y
Olhofer, M
Sendhoff, B
Date : 26 August 2016
DOI : 10.1109/TCYB.2016.2600577
Copyright Disclaimer : (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 16 Aug 2016 09:41
Last Modified : 14 Nov 2018 11:15

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800