将质量多样性优化转化为多目标问题，用成熟方法高效求解。
Quality-Diversity Optimization as Multi-Objective Optimization
- 把QD问题重构成海量目标的多目标优化问题。
- 实验显示性能媲美顶尖QD算法，且可复用经典优化方法。
- 适合研究者快速应用先进多目标技术解决多样性探索任务。
质量-多样性（QD）优化旨在发现一组高性能且在用户定义的行为空间中具有多样性的解决方案。该范式已引发广泛关注，并在机器人控制、创造性设计和对抗样本生成等领域展现出实际价值。近年来提出了多种设计原理各异的QD算法。本文并未提出新QD算法，而是通过将QD优化重新表述为具有大量优化目标的多目标优化（MOO）问题，建立这一新视角。基于此，可直接采用成熟的MOO方法，特别是基于集合的标量化技术，通过协作搜索过程求解QD问题。我们进一步提供了理论分析，证明该方法继承了MOO的理论保障，同时具备适合QD优化的优良性质。在多个QD应用场景上的实验表明，该方法性能可与当前最先进的QD算法相媲美。
原文摘要 · Abstract (English)
The Quality-Diversity (QD) optimization aims to discover a collection of high-performing solutions that simultaneously exhibit diverse behaviors within a user-defined behavior space. This paradigm has stimulated significant research interest and demonstrated practical utility in domains including robot control, creative design, and adversarial sample generation. A variety of QD algorithms with distinct design principles have been proposed in recent years. Instead of proposing a new QD algorithm, this work introduces a novel reformulation by casting the QD optimization as a multi-objective optimization (MOO) problem with a huge number of optimization objectives. By establishing this connection, we enable the direct adoption of well-established MOO methods, particularly set-based scalarization techniques, to solve QD problems through a collaborative search process. We further provide a theoretical analysis demonstrating that our approach inherits theoretical guarantees from MOO while providing desirable properties for the QD optimization. Experimental studies across several QD applications confirm that our method achieves performance competitive with state-of-the-art QD algorithms.
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