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Evolutionary multi-objective optimization (EMO) algorithms have beendemonstrated to be effective in solving multi-criteria decision-makingproblems. In real-world applications, analysts often employ several algorithmsconcurrently and compare their solution sets to gain insight into thecharacteristics of different algorithms and explore a broader range of feasiblesolutions. However, EMO algorithms are typically treated as black boxes,leading to difficulties in performing detailed analysis and comparisons betweenthe internal evolutionary processes. Inspired by the successful application ofvisual analytics tools in explainable AI, we argue that interactivevisualization can significantly enhance the comparative analysis betweenmultiple EMO algorithms. In this paper, we present a visual analytics frameworkthat enables the exploration and comparison of evolutionary processes in EMOalgorithms. Guided by a literature review and expert interviews, the proposedframework addresses various analytical tasks and establishes a multi-facetedvisualization design to support the comparative analysis of intermediategenerations in the evolution as well as solution sets. We demonstrate theeffectiveness of our framework through case studies on benchmarking andreal-world multi-objective optimization problems to elucidate how analysts canleverage our framework to inspect and compare diverse algorithms.

A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization