Adaptive Differential Evolution Applied to Point Matching 2D GIS Data

Date
2015-12-07
Authors
Khan, N.
Neri, Ferrante
Ahmadi, Samad
Journal Title
Journal ISSN
ISSN
Volume Title
Publisher
IEEE
Peer reviewed
Yes
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.
Description
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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
Research Institute
Institute of Artificial Intelligence (IAI)