Multi-strategy Differential Evolution

Anil Yaman, Giovanni Iacca, Matthew Coler, George Fletcher, Mykola Pechenizkiy

OnderzoeksoutputAcademicpeer review

3 Citaten (Scopus)
227 Downloads (Pure)

Samenvatting

We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.
Originele taal-2English
TitelApplications of Evolutionary Computation
UitgeverijSpringer
Pagina's617-633
Aantal pagina's17
ISBN van elektronische versie978-3-319-77538-8
ISBN van geprinte versie978-3-319-77537-1
DOI's
StatusPublished - 8-mrt-2018
EvenementEvoStar - Amsterdam, Netherlands
Duur: 1-apr-2017 → …
http://www.evostar.org/2017/

Conference

ConferenceEvoStar
Verkorte titelevo*2017
Land/RegioNetherlands
StadAmsterdam
Periode01/04/2017 → …
Internet adres

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