Coverage problems and visibility regions on topographic surfaces
Annals of Operations Research
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Artificial Intelligence in Geography
Artificial Intelligence in Geography
Evolutionary Modeling of Spatial Information
Evolutionary Modeling of Spatial Information
Optimal Placements of Flexible Objects: An Adaptive Simulated Annealing Approach
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
Multi-visibility maps of triangulated terrains
International Journal of Geographical Information Science
Cloud Theory Based Simulated Annealing Algorithm for Multiple Observers Sitting on Terrain Problem
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Least visible path analysis in raster terrain
International Journal of Geographical Information Science
Analysing potential field data using visibility
Computers & Geosciences
International Journal of Mobile Network Design and Innovation
Efficient viewshed computation on terrain in external memory
Geoinformatica
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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The calculation of viewsheds is a routine operation in geographic information systems and is used in a wide range of applications. Many of these involve the siting of features, such as radio masts, which are part of a network and yet the selection of sites is normally done separately for each feature. The selection of a series of locations which collectively maximise the visual coverage of an area is a combinatorial problem and as such cannot be directly solved except for trivial cases. In this paper, two strategies for tackling this problem are explored. The first is to restrict the search to key topographic points in the landscape such as peaks, pits and passes. The second is to use heuristics which have been applied to other maximal coverage spatial problems such as location-allocation. The results show that the use of these two strategies results in a reduction of the computing time necessary by two orders of magnitude, but at the cost of a loss of 10% in the area viewed. Three different heuristics were used, of which Simulated Annealing produced the best results. However the improvement over a much simpler fast-descent swap heuristic was very slight, but at the cost of greatly increased running times.