Viz: A visual analysis suite for explaining local search behavior

Abstract

NP-hard combinatorial optimization problems are common in real life. Due to their intractability, local search algorithms are often used to solve such problems. Since these algorithms are heuristic-based, it is hard to understand how to improve or tune them. We propose an interactive visualization tool, Viz, meant for understanding the behavior of local search. Viz uses animation of abstract search trajectories with other visualizations which are also animated in a VCR-like fashion to graphically playback the algorithm behavior. It combines generic visualizations applicable on arbitrary algorithms with algorithm and problem specific visualizations. We use a variety of techniques such as alpha blending to reduce visual clutter and to smooth animation, highlights and shading, automatically generated index points for playback, and visual comparison of two algorithms. The use of multiple viewpoints can be an effective way of understanding search behavior and highlight algorithm behavior which might otherwise be hidden. Copyright 2006 ACM.