Human-Guided Enhancement of a Stochastic Local Search: Visualization and Adjustment of 3D Pheromone

• 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.

Ämnesord

NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)

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