Adaptive Differential Evolution Applied to Point Matching 2D GIS Data

De Montfort University Open Research Archive

Show simple item record Khan, N. en Neri, Ferrante en Ahmadi, Samad en 2016-04-18T10:18:16Z 2016-04-18T10:18:16Z 2015-12-07
dc.identifier.citation Khan, N., Neri, F. and Ahmadi, S. (2015) Adaptive Differential Evolution Applied to Point Matching 2D GIS Data. 2015 IEEE Symposium Series on Computational Intelligence, pp. 1719-1726 en
dc.identifier.isbn 9781479975600
dc.description.abstract The impetus behind data analytics and integration is the need for greater insight and data visibility, but since a growing share of our data is multimedia, there is a parallel need for methods that can align multimedia data. This paper explores georeferencing, which is used to combine spatial datasets and used here to align map images to 2D GIS models. This paper surveys various approaches for building the key components of a georeferencing solution, notes their strengths and weaknesses, and comments on their trajectory to help orient future work. The implementation presented here uses Hough transforms for feature detection, nearest neighbor correspondences with simplistic similarity measures, and a population based optimizer. The comparison among metaheuristics has shown that Differential Evolution (DE) frameworks appear especially suited for this problem. In particular, the controlled randomization of DE parameters appears to display the best performance in terms of execution time and competitive performance in terms of function evaluations even with respect to more complex memetic implementations. en
dc.language.iso en en
dc.publisher IEEE en
dc.title Adaptive Differential Evolution Applied to Point Matching 2D GIS Data en
dc.type Conference en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.funder N/A en
dc.projectid N/A en
dc.cclicence N/A en

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