Coordinate exchange (CEXCH) is a popular algorithm for generating exact optimal experimental designs. The authors of CEXCH advocated for a highly greedy implementation - one that exchanges and optimizes single element coordinates of the design matrix. We revisit the effect of greediness on CEXCHs efficacy for generating highly efficient designs. We implement the single-element CEXCH (most greedy), a design-row (medium greedy) optimization exchange, and particle swarm optimization (PSO; least greedy) on 21 exact response surface design scenarios, under the $D$- and $I-$criterion, which have well-known optimal designs that have been reproduced by several researchers. We found essentially no difference in performance of the most greedy CEXCH and the medium greedy CEXCH. PSO did exhibit better efficacy for generating $D$-optimal designs, and for most $I$-optimal designs than CEXCH, but not to a strong degree under our parametrization. This work suggests that further investigation of the greediness dimension and its effect on CEXCH efficacy on a wider suite of models and criterion is warranted.

翻译：坐标交换（CEXCH）是生成精确最优实验设计的常用算法。CEXCH的提出者倡导一种高度贪婪的实现方式——即每次交换并优化设计矩阵中的单个元素坐标。我们重新审视了贪婪性对CEXCH生成高效设计效能的影响。在21个精确响应曲面设计场景中，我们分别实施了单元素CEXCH（最贪婪）、设计行（中等贪婪）优化交换以及粒子群优化（PSO；最不贪婪），并采用了$D$准则和$I$准则——这些场景具有多位研究者复现的知名最优设计。我们发现最贪婪CEXCH与中等贪婪CEXCH在性能上基本没有差异。PSO在生成$D$-最优设计时表现出比CEXCH更好的效能，对于大多数$I$-最优设计也是如此，但在我们的参数化设置下其优势并不显著。本研究表明，有必要在更广泛的模型和准则集合中进一步探究贪婪性维度及其对CEXCH效能的影响。