Nature-inspired optimization algorithms: challenges and open problems
Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556
(2020)
Nature-inspired optimization algorithms: challenges and open problems.
Journal of Computational Science, 46
, 101104.
ISSN 1877-7503
[Article]
(doi:10.1016/j.jocs.2020.101104)
|
PDF
- Final accepted version (with author's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0. Download (269kB) | Preview |
Abstract
Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.
Item Type: | Article |
---|---|
Research Areas: | A. > School of Science and Technology > Design Engineering and Mathematics |
Item ID: | 29512 |
Notes on copyright: | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. |
Useful Links: | |
Depositing User: | Xin-She Yang |
Date Deposited: | 12 Mar 2020 12:28 |
Last Modified: | 02 Dec 2021 13:33 |
URI: | https://eprints.mdx.ac.uk/id/eprint/29512 |
Actions (login required)
![]() |
View Item |
Statistics
Additional statistics are available via IRStats2.