Abstract
This article is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely, decomposition methods and hybridization methods, such as memetic algorithms and local search. In this part, we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multiobjective optimization, constraint handling, overlapping components, the component imbalance issue and benchmarks, and applications. The article also includes a discussion on pitfalls and challenges of the current research and identifies several potential areas of future research. © 1997-2012 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 823-843 |
| Number of pages | 21 |
| Journal | IEEE Transactions on Evolutionary Computation |
| Volume | 26 |
| Issue number | 5 |
| Early online date | 25 Nov 2021 |
| DOIs | |
| Publication status | Published - Oct 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1997-2012 IEEE.
Funding
This work was supported in part by the Australian Research Council (ARC) Discovery under Grant DP180101170 and Grant DP190101271; in part by the Shenzhen Science and Technology Program under Grant KQTD2016112514355531; in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386; and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.
Keywords
- Black-box optimization
- evolutionary optimization
- large-scale global optimization
- metaheuristics