Yazdani, Danial , Yazdani, Donya, Branke, Juergen, Nabi Omidvar, Mohammad, Gandomi, Amir Hossein and Yao, Xin (2022) Robust optimization over time by estimating robustness of promising regions. IEEE Transactions on Evolutionary Computation, 27 (3). pp. 657-670. doi:10.1109/TEVC.2022.3180590 ISSN 1089-778X.

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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 toward 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 article, a multipopulation ROOT method is proposed with two novel components: 1) a robustness estimation component that estimates robustness of the promising regions and 2) a dual-mode computational resource allocation component to manage subpopulations 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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Robust optimization, Evolutionary computation , Genetic algorithms
Journal or Publication Title:	IEEE Transactions on Evolutionary Computation
Publisher:	IEEE
ISSN:	1089-778X
Official Date:	7 June 2022
Dates:	Date Event 7 June 2022 Published 20 May 2022 Accepted
Volume:	27
Number:	3
Page Range:	pp. 657-670
DOI:	10.1109/TEVC.2022.3180590
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2022 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.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	23 May 2022
Date of first compliant Open Access:	23 May 2022
Related URLs:	Publisher

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