Omidvar, MN orcid.org/0000-0003-1944-4624, Li, X and Yao, X (2022) A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part I. IEEE Transactions on Evolutionary Computation, 26 (5). pp. 802-822. ISSN 1089-778X
Abstract
Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly, population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part I of the series covers two major algorithmic approaches to large-scale global optimization: 1) problem decomposition and 2) memetic algorithms. Part II of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally, touches upon the pitfalls and challenges of current research and identifies several potential areas for future research.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. |
Keywords: | black-box optimization; evolutionary optimization; large-scale global optimization; metaheuristics |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Accounting & Finance Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2021 16:20 |
Last Modified: | 21 Dec 2022 10:28 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TEVC.2021.3130838 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180820 |