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Human-Guided Enhancement of a Stochastic Local Search: Visualization and Adjustment of 3D Pheromone
University of California, Davis, CA, USA .
University of California, Davis, CA, USA .
University of California, Davis, CA, USA .
Zuse Institue Berlin.ORCID iD: 0000-0001-7285-0483
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2007 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we describe user interaction with an optimization algorithm via a sophisticated visualization interface that we created for this purpose. Our primary interest is the tool itself. We demonstrate that a user wielding this tool can find ways to improve the performance of an ant colony optimization (ACO) algorithm as applied to a problem of finding 3D paths in the presence of impediments [14]. One part of a solution method can be to find a path on a grid. Of course, there are near linear time algorithms for the shortest path that have been applied to problems that are quite large. However, for a grid in three dimensions with arcs on the axes and diagonals, the problems can become extremely large as resolution is increased and heuristics thus make sense (see, e.g., [6] for state-of-the art algorithms where pre-processing is possible). Ant colony optimization (see, e.g., [4,5]) is ideally suited to such a problem.

Place, publisher, year, edition, pages
2007. Vol. 4638, 182-186 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 4638
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-128061DOI: 10.1007/978-3-540-74446-7_14ISBN: 978-3-540-74445-0 (print)ISBN: 978-3-540-74446-7 (print)OAI: oai:DiVA.org:liu-128061DiVA: diva2:928790
Conference
International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007. Proceedings
Available from: 2016-05-16 Created: 2016-05-16 Last updated: 2016-05-30

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Hotz, Ingrid
Computational Mathematics

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CiteExportLink to record
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Citation style
  • apa
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  • vancouver
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More styles
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  • nn-NB
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Output format
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