Yazdani, D, Yazdani, D, Branke, J et al. (3 more authors) (2022) Robust Optimization Over Time by Estimating Robustness of Promising Regions. IEEE Transactions on Evolutionary Computation, 27 (3). pp. 657-670. ISSN 1089-778X
Abstract
Many real-world optimization problems are dynamic. The field of robust optimization over time (ROOT) deals with dynamic optimization problems in which frequent changes of the deployed solution are undesirable. This can be due to the high cost of switching the deployed solutions, the limitation of the needed resources to deploy such new solutions, and/or the system being intolerant towards frequent changes of the deployed solution. In the considered ROOT problems in this article, the main goal is to find solutions that maximize the average number of environments where they remain acceptable. In the state-of-the-art methods developed to tackle these problems, the decision makers/metrics used to select solutions for deployment mostly make simplifying assumptions about the problem instances. Besides, the current methods all use the population control components which have been originally designed for tracking the global optimum over time without taking any robustness considerations into account. In this paper, a multi-population ROOT method is proposed with two novel components: a robustness estimation component that estimates robustness of the promising regions, and a dual-mode computational resource allocation component to manage sub-populations by taking several factors, including robustness, into account. Our experimental results demonstrate the superiority of the proposed method over other state-of-the-art approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | Robust optimization over time , Evolutionary dynamic optimization , Multi-population , Computational resource allocation , Robustness estimation |
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: | 26 May 2022 12:25 |
Last Modified: | 23 May 2024 14:10 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TEVC.2022.3180590 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187349 |
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