Show simple item record

dc.contributor.authorUher, Vojtěch
dc.contributor.authorGajdoš, Petr
dc.contributor.authorRadecký, Michal
dc.contributor.authorSnášel, Václav
dc.date.accessioned2017-01-05T07:28:25Z
dc.date.available2017-01-05T07:28:25Z
dc.date.issued2016
dc.identifier.citationComputational Intelligence and Neuroscience. 2016, art. no. 6329530.cs
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.urihttp://hdl.handle.net/10084/116565
dc.description.abstractThe Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.cs
dc.format.extent3338860 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoencs
dc.publisherHindawics
dc.relation.ispartofseriesComputational Intelligence and Neurosciencecs
dc.relation.urihttp://dx.doi.org/10.1155/2016/6329530cs
dc.rightsCopyright © 2016 Vojtěch Uher et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.titleUtilization of the discrete differential evolution for optimization in multidimensional point cloudscs
dc.typearticlecs
dc.identifier.doi10.1155/2016/6329530
dc.rights.accessopenAccess
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.firstpageart. no. 6329530cs
dc.identifier.wos000388857000001


Files in this item

This item appears in the following Collection(s)

Show simple item record

Copyright © 2016 Vojtěch Uher et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright © 2016 Vojtěch Uher et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.