dc.contributor.author | Santana, C | |
dc.contributor.author | Keedwell, E | |
dc.contributor.author | Menezes, R | |
dc.date.accessioned | 2020-04-29T14:07:53Z | |
dc.date.issued | 2020-07-12 | |
dc.description.abstract | The growing number of novel swarm-based meta-heuristics has
been raising debates regarding their novelty. These algorithms often claim to be inspired by different concepts from nature but the
proponents of these seldom demonstrate whether the novelty goes
beyond the natural inspiration. In this work, we employed the concept of Interaction Networks to capture the interaction patterns that
take place in the algorithms during the optimisation process. The
analyses of these networks reveal aspects of the algorithm such as
the tendency to achieve premature convergence, population diversity, and stability. Furthermore, we propose the usage of Portrait
Divergence, a state-of-the-art metric to assess the structural similarities between networks. Using this approach to analyse the Cat
Swarm Optimisation algorithm, we were able to identify some of
the algorithm’s characteristics, assess the impact of one its parameters, and compare it to two other well-known methods (Particle
Swarm Optimisation and Artificial Bee Colony). Lastly, we discuss
the relationship between the interaction network and the performance of the algorithm and demonstrate the similarities between
Cat Swarms and Particle Swarms. | en_GB |
dc.description.sponsorship | University of Exeter | en_GB |
dc.identifier.citation | GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, June 2020, pp. 31–39 | en_GB |
dc.identifier.doi | 10.1145/3377930.3390201 | |
dc.identifier.uri | http://hdl.handle.net/10871/120852 | |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computing Machinery (ACM) | en_GB |
dc.rights | © 2020 Copyright held by the owner/author(s).Permission to make digital or hard copies of part or all of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s). | en_GB |
dc.subject | Swarm Intelligence | en_GB |
dc.subject | Complex Networks | en_GB |
dc.subject | Interaction Networks | en_GB |
dc.subject | Cat Swarm Optimisation | en_GB |
dc.title | An approach to assess swarm intelligence algorithms based on complex networks | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2020-04-29T14:07:53Z | |
dc.description | This is the author accepted manuscript. The final version is available from ACM via the DOI in this record | en_GB |
dc.description | Genetic and Evolutionary Computation Conference (GECCO ’20), 8-12 July 2020, Cancún, Mexico | |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2020-03-20 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2020-03-20 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2020-04-28T19:55:30Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-08-10T12:13:52Z | |
refterms.panel | B | en_GB |