Human-Guided Enhancement of a Stochastic Local Search: Visualization and Adjustment of 3D Pheromone
2007 (English)Conference paper (Refereed)
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 . 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.,  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.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 4638
IdentifiersURN: urn:nbn:se:liu:diva-128061DOI: 10.1007/978-3-540-74446-7_14ISBN: 978-3-540-74445-0ISBN: 978-3-540-74446-7OAI: oai:DiVA.org:liu-128061DiVA: diva2:928790
International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007. Proceedings