Abstract
Dynamic optimization, for which the objective functions change over time, has attracted intensive investigations due to the inherent uncertainty associated with many real-world problems. For its robustness with respect to noise, evolutionary algorithms (EAs) have been expected to have great potential for dynamic optimization. Many dynamic optimization methods, such as diversity-driven methods, memory methods, and prediction methods have been proposed based on EAs to deal with environmental changes. However, they face difficulties in adapting to fast changes in dynamic optimization as EAs normally need quite a few fitness evaluations to find a near-optimum solution. To address this issue, this article proposes a new framework of applying EAs in the context of dynamic optimization to deal with fast changing environments. We suggest that instead of online evolving (searching) solutions for the ever-changing objective function, EAs are more suitable for acquiring an archive of solutions in an offline way, which could be adopted to construct a system to provide high-quality solutions efficiently in a dynamic environment. To be specific, we formulate the offline search as a static set-oriented optimization problem. Then, a set of solutions is obtained by an EA for this set-oriented optimization problem. After this, the obtained solution set is adopted to do fast adaptation to the corresponding dynamic optimization problem. The general framework is instantiated for continuous dynamic-constrained optimization problems, and the empirical results show the potential of the proposed framework. The superiority of the framework is also verified on a dynamic vehicle routing problem with changing demands.
Original language | English |
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Pages (from-to) | 431-445 |
Number of pages | 15 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 26 |
Issue number | 3 |
Early online date | 12 Aug 2021 |
DOIs | |
Publication status | Published - Jun 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1997-2012 IEEE.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 61906082; in part by the Honda Research Institute Europe (HRI-EU); in part by the Guangdong Provincial Key Laboratory under Grant 2020B121201001; in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386; in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531; in part by the Research Institute of Trustworthy Autonomous Systems (RITAS); in part by the Science and Technology Commission of Shanghai Municipality under Grant 19511120602; in part by the National Leading Youth Talent Support Program of China; and in part by the MOE University Scientific-Technological Innovation Plan Program.
Keywords
- Dynamic constrained optimization
- dynamic optimization
- local search
- negatively correlated search
- set-oriented optimization