Impact of Chaos Functions on Modern Swarm Optimizers

E Emary and Hossam M Zawbaa

PLOS ONE, 2016, vol. 11, issue 7, 1-26

Abstract: Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.

Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158738 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 58738&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0158738

DOI: 10.1371/journal.pone.0158738

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().