Letting ants labeling point features [sic.: for 'labeling' read 'label']

Authors:
M. Schreyer;G. R. Raidl
Affiliations:
Inst. of Comput. Graphics & Algorithms, Vienna Univ. of Technol., Austria;Inst. of Comput. Graphics & Algorithms, Vienna Univ. of Technol., Austria
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

Citing 0
Cited 4

A hybrid approach for feature subset selection using neural networks and ant colony optimization

Expert Systems with Applications: An International Journal
Lagrangean relaxation with clusters for point-feature cartographic label placement problems

Computers and Operations Research
A greedy randomized adaptive search procedure for the point-feature cartographic label placement

Computers & Geosciences
Two cooperative ant colonies for feature selection using fuzzy models

Expert Systems with Applications: An International Journal

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Abstract

This paper describes an ant colony system (ACS) for labeling point features. A pre-processing step reduces the search space in a safe way. The ACS applies local improvement and masking, a technique that focuses the optimization on critical regions. Empirical results indicate that the ACS reliably identifies high-quality solutions which are in many cases better than those of a state-of-the-art genetic algorithm for point-feature labeling.