Giagkiozis, I. orcid.org/0000-0002-5521-2196, Purshouse, R.C. orcid.org/0000-0001-5880-1925 and Fleming, P.J. orcid.org/0000-0001-9837-8404 (2014) Towards understanding the cost of adaptation in decomposition-based optimization algorithms. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics. 2013 IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct 2013, Manchester, United Kingdom. Institute of Electrical and Electronics Engineers (IEEE) , pp. 615-620.
Abstract
Decomposition-based methods are an increasingly popular choice for a posteriori multi-objective optimization. However the ability of such methods to describe a trade-off surface depends on the choice of weighting vectors defining the set of subproblems to be solved. Recent adaptive approaches have sought to progressively modify the weighting vectors to obtain a desirable distribution of solutions. This paper argues that adaptation imposes a non-negligible cost - in terms of convergence - on decomposition-based algorithms. To test this hypothesis, the process of adaptation is abstracted and then subjected to experimentation on established problems involving between three and 11 conflicting objectives. The results show that adaptive approaches require longer traversals through objectivespace than fixed-weight approaches. Since fixed weights cannot, in general, be specified in advance, it is concluded that the new wave of decomposition-based methods offer no immediate panacea to the well-known conflict between convergence and distribution afflicting Pareto-based a posteriori methods.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2013 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. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Decision support systems; multi-objective optimization; decomposition; adaptation |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Apr 2024 11:25 |
Last Modified: | 29 Apr 2024 11:25 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/smc.2013.110 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212050 |