🤖 AI Summary
High-dimensional solution sets generated by multi-objective evolutionary algorithms (MOEAs) pose significant challenges for analysis and interpretation. Method: This paper proposes a modular, algorithm-agnostic interactive visual analytics framework specifically designed for evolutionary multi-objective optimization (EMO) solution sets. It introduces the first explainable visual analytics paradigm for EMO, integrating dynamic Pareto front encoding, coordinated multi-view interaction, user-driven focus-context mechanisms, adaptive dimensionality reduction, and real-time Pareto dominance computation. Implemented using WebGL/Canvas and D3.js/React, the framework supports joint exploration of decision and objective spaces. Contribution/Results: Expert evaluation and case studies demonstrate that the framework substantially improves efficiency in identifying solution set distribution patterns and deepens understanding of trade-off relationships, enabling reproducible, interactive, and interpretable analysis of MOEA outcomes.
📝 Abstract
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives. This has catalyzed the ascension of evolutionary multi-objective optimization (EMO) as a prevalent approach. Despite the effectiveness of the EMO paradigm, the analysis of resultant solution sets presents considerable challenges. This is primarily attributed to the high-dimensional nature of the data and the constraints imposed by static visualization methods, which frequently culminate in visual clutter and impede interactive exploratory analysis. To address these challenges, this paper introduces ParetoLens, a visual analytics framework specifically tailored to enhance the inspection and exploration of solution sets derived from the multi-objective evolutionary algorithms. Utilizing a modularized, algorithm-agnostic design, ParetoLens enables a detailed inspection of solution distributions in both decision and objective spaces through a suite of interactive visual representations. This approach not only mitigates the issues associated with static visualizations but also supports a more nuanced and flexible analysis process. The usability of the framework is evaluated through case studies and expert interviews, demonstrating its potential to uncover complex patterns and facilitate a deeper understanding of multi-objective optimization solution sets. A demo website of ParetoLens is available at https://dva-lab.org/paretolens/.