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. © 1997-2012 IEEE.
Bibliographical noteThis work was supported in part by the Research Institute of Trustworthy Autonomous Systems, Guangdong Provincial Key Laboratory under Grant 2020B121201001; in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386; and in part by the Shenzhen Science and Technology Program under Grant KQTD2016112514355531.
- Computational resource allocation (CRA)
- evolutionary dynamic optimization (EDO)
- robust optimization over time (ROOT)
- robustness estimation