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dc.contributor.authorLi, Zhijianen
dc.contributor.authorGuo, Jingleien
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2016-11-28T10:58:23Z
dc.date.available2016-11-28T10:58:23Z
dc.date.issued2016-09
dc.identifier.citationLi, Z., Guo, J. and Yang, S. (2016) Improving the JADE algorithm by clustering successful parameters. International Journal of Wireless and Mobile Computing, 11 (3), pp. 190-197en
dc.identifier.issn1741-1084
dc.identifier.urihttp://hdl.handle.net/2086/12989
dc.description.abstractDifferential evolution (DE) is one of the most powerful and popular evolutionary algorithms for real parameter global optimisation problems. However, the performance of DE greatly depends on the selection of control parameters, e.g., the population size, scaling factor and crossover rate. How to set these parameters is a challenging task because they are problem dependent. In order to tackle this problem, a JADE variant, denoted CJADE, is proposed in this paper. In the proposed algorithm, the successful parameters are clustered with the k-means clustering algorithm to reduce the impact of poor parameters. Simulation results show that CJADE is better than, or at least comparable with, several state-of-the-art DE algorithms.en
dc.language.isoen_USen
dc.publisherInderscience Publishersen
dc.subjectdifferential evolution algorithmen
dc.subjectk-meansen
dc.subjectsuccessful parametersen
dc.titleImproving the JADE algorithm by clustering successful parametersen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1504/ijwmc.2016.081159
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-09-01en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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