A Comparative Visual Analytics Framework for Evaluating Evolutionary
Processes in Multi-objective Optimization
- URL: http://arxiv.org/abs/2308.05640v1
- Date: Thu, 10 Aug 2023 15:32:46 GMT
- Title: A Comparative Visual Analytics Framework for Evaluating Evolutionary
Processes in Multi-objective Optimization
- Authors: Yansong Huang, Zherui Zhang, Ao Jiao, Yuxin Ma, Ran Cheng
- Abstract summary: We present a visual analytics framework that enables the exploration and comparison of evolutionary processes in EMO algorithms.
We demonstrate the effectiveness of our framework through case studies on benchmarking and real-world multi-objective optimization problems.
- Score: 7.906582204901926
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Evolutionary multi-objective optimization (EMO) algorithms have been
demonstrated to be effective in solving multi-criteria decision-making
problems. In real-world applications, analysts often employ several algorithms
concurrently and compare their solution sets to gain insight into the
characteristics of different algorithms and explore a broader range of feasible
solutions. However, EMO algorithms are typically treated as black boxes,
leading to difficulties in performing detailed analysis and comparisons between
the internal evolutionary processes. Inspired by the successful application of
visual analytics tools in explainable AI, we argue that interactive
visualization can significantly enhance the comparative analysis between
multiple EMO algorithms. In this paper, we present a visual analytics framework
that enables the exploration and comparison of evolutionary processes in EMO
algorithms. Guided by a literature review and expert interviews, the proposed
framework addresses various analytical tasks and establishes a multi-faceted
visualization design to support the comparative analysis of intermediate
generations in the evolution as well as solution sets. We demonstrate the
effectiveness of our framework through case studies on benchmarking and
real-world multi-objective optimization problems to elucidate how analysts can
leverage our framework to inspect and compare diverse algorithms.
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