Sun, Y, Li, X, Ernst, A et al. (1 more author) (2019) Decomposition for Large-scale Optimization Problems with Overlapping Components. In: 2019 IEEE Congress on Evolutionary Computation (CEC). 2019 IEEE Congress on Evolutionary Computation (CEC), 10-13 Jun 2019, Wellington, New Zealand. IEEE , pp. 326-333. ISBN 9781728121536
Abstract
In this paper we use a divide-and-conquer approach to tackle large-scale optimization problems with overlapping components. Decomposition for an overlapping problem is challenging as its components depend on one another. The existing decomposition methods typically assign all the linked decision variables into one group, thus cannot reduce the original problem size. To address this issue we modify the Recursive Differential Grouping (RDG) method to decompose overlapping problems, by breaking the linkage at variables shared by multiple components. To evaluate the efficacy of our method, we extend two existing overlapping benchmark problems considering various level of overlap. Experimental results show that our method can greatly improve the search ability of an optimization algorithm via divide-and-conquer, and outperforms RDG, random decomposition as well as other state-of-the-art methods. We further evaluate our method using the CEC'2013 benchmark problems and show that our method is very competitive when equipped with a component optimizer.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. |
Keywords: | Cooperative co-evolution, large-scale continuous optimization, overlapping problem, variable interaction, problem decomposition |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Accounting & Finance Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Jan 2020 09:57 |
Last Modified: | 07 Feb 2020 02:47 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/cec.2019.8790204 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156232 |