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Algorithms for data visualizations are essential tools for transforming datainto useful narratives. Unfortunately, very few visualization algorithms canhandle the large datasets of many real-world scenarios. In this study, weaddress the visualization of these datasets as a Multi-Objective OptimizationProblem. We propose mQAPViz, a divide-and-conquer multi-objective optimizationalgorithm to compute large-scale data visualizations. Our method employs theMulti-Objective Quadratic Assignment Problem (mQAP) as the mathematicalfoundation to solve the visualization task at hand. The algorithm appliesadvanced sampling techniques originating from the field of machine learning andefficient data structures to scale to millions of data objects. The algorithmallocates objects onto a 2D grid layout. Experimental results on real-world andlarge datasets demonstrate that mQAPViz is a competitive alternative toexisting techniques.

mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations