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Robust optimization over time : a critical review
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Yazdani, Danial, Omidvar, Mohammad Nabi, Yazdani, Donya, Branke, Jürgen, Nguyen, Trung Thanh, Gandomi, Amir H, Jin, Yaochu and Yao, Yao (2023) Robust optimization over time : a critical review. IEEE Transactions on Evolutionary Computation . doi:10.1109/TEVC.2023.3306017 ISSN 1089-778X. (In Press)
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Official URL: https://doi.org/10.1109/TEVC.2023.3306017
Abstract
Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system’s inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive review of them. This survey also reviews ROOT benchmarks and performance indicators. Finally, we identify several future research directions.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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Library of Congress Subject Headings (LCSH): | Robust optimization, Evolutionary computation, Genetic algorithms, Computational intelligence, Evolutionary programming (Computer science) | ||||||
Journal or Publication Title: | IEEE Transactions on Evolutionary Computation | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1089-778X | ||||||
Official Date: | 17 August 2023 | ||||||
Dates: |
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DOI: | 10.1109/TEVC.2023.3306017 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 3 August 2023 | ||||||
Date of first compliant Open Access: | 4 August 2023 | ||||||
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