Diversity preservation plays an important role in the design of multi-objective evolutionary algorithms, but the diversity performance assessment of these algorithms remains challenging. To address this issue, this paper proposes a performance metric and a multi-objective test suite for the diversity assessment of multiobjective evolutionary algorithms. The proposed metric assesses both the evenness and spread of a solution set by projecting it to a lower-dimensional hypercube and calculating the "volume" of the projected solution set. The proposed test suite contains eight benchmark problems, which pose stiff challenges for existing algorithms to obtain a diverse solution set. Experimental studies demonstrate that the proposed metric can assess the diversity of a solution set more precisely than existing ones, and the proposed test suite can be used to effectively distinguish between algorithms with respect to their diversity performance.

中文翻译：

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多目标进化算法的多样性评估：性能指标和基准问题 [研究前沿]

多样性保持在多目标进化算法的设计中起着重要作用，但这些算法的多样性性能评估仍然具有挑战性。为了解决这个问题，本文提出了一个性能指标和一个多目标测试套件，用于多目标进化算法的多样性评估。建议的度量通过将解集投影到低维超立方体并计算投影解集的“体积”来评估解集的均匀度和散布度。提议的测试套件包含八个基准问题，这对现有算法获得不同的解决方案集提出了严峻的挑战。实验研究表明，所提出的度量可以比现有度量更准确地评估解决方案集的多样性，