  Algorithms for data visualizations are essential tools for transforming data
into useful narratives. Unfortunately, very few visualization algorithms can
handle the large datasets of many real-world scenarios. In this study, we
address the visualization of these datasets as a Multi-Objective Optimization
Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization
algorithm to compute large-scale data visualizations. Our method employs the
Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical
foundation to solve the visualization task at hand. The algorithm applies
advanced sampling techniques originating from the field of machine learning and
efficient data structures to scale to millions of data objects. The algorithm
allocates objects onto a 2D grid layout. Experimental results on real-world and
large datasets demonstrate that mQAPViz is a competitive alternative to
existing techniques.
