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 , 101104. ISSN 1877-7503 [Article] (Published online first) (doi:10.1016/j.jocs.2020.101104)

[img] PDF - Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only until 6 March 2021.
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives.

Download (269kB) |

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: 04 Apr 2020 07:44
URI: https://eprints.mdx.ac.uk/id/eprint/29512

Actions (login required)

View Item View Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year