Animorphic ensemble optimization: a large-scale island model
Name
521_2022_7878_ReferencePDF.pdf
Size
3.75 MB
Format
Adobe PDF
Checksum (MD5)
71f83367e82ecabc3ae58ac8c84da2c9
Author(s)
Price, Dean
Radaideh, Majdi I.
Date Issued
October 8, 2022
Publisher
Springer London
Citation
Price, Dean and Radaideh, Majdi I. 2022. "Animorphic ensemble optimization: a large-scale island model."
Version
Author's final manuscript
Abstract
Abstract
In this paper, a flexible large-scale ensemble-based optimization algorithm is presented for complex optimization problems. According to the no free lunch theorem, no single optimization algorithm demonstrates superior performance across all optimization problems. Therefore, with the animorphic ensemble optimization (AEO) algorithm presented here, a set of algorithms can be used as an ensemble which demonstrate stronger performance across a wider range of optimization problems than any standalone algorithm. AEO is a high-level ensemble designed to handle large ensembles using a well-defined stochastic migration process. The high-level nature of AEO allows for an arbitrary number of diverse standalone algorithms to interface with one another through an island model interface strategy, where various populations change size according to the performance of the algorithm associated with each population. In this study, AEO is demonstrated using ensembles of both evolutionary and swarm algorithms such as differential evolution, particle swarm, gray wolf optimization, moth-flame optimization, and more, and strong performance is observed. Quantitative diagnostics metrics to describe the migration of individuals across populations are also presented and observed with application to some test problems. In the end, AEO demonstrated strong consistent performance across more than 150 benchmark functions of 10–50 dimensions.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s00521-022-07878-y
