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.

翻译：质量-多样性（QD）优化旨在发现一组在用户定义的行为空间中同时展现出多样化行为的高性能解决方案。这一范式已激发广泛的研究兴趣，并在机器人控制、创意设计和对抗样本生成等领域展现出实际应用价值。近年来，研究者提出了多种具有不同设计原则的QD算法。本文并未提出新的QD算法，而是通过将QD优化重构为具有海量优化目标的多目标优化（MOO）问题，引入了一种新颖的表述方式。通过建立这种联系，我们能够直接采用成熟的MOO方法（特别是基于集合的标量化技术），通过协作搜索过程来解决QD问题。我们进一步提供了理论分析，证明该方法继承了MOO的理论保证，同时为QD优化提供了理想特性。在多个QD应用场景中的实验研究表明，本方法实现了与最先进QD算法相竞争的性能表现。