When a model-based approach is appropriate, an optimal design can guide how to collect data judiciously for making reliable inference at minimal cost. However, finding optimal designs for a statistical model with several possibly interacting factors can be both theoretically and computationally challenging, and this issue is rarely discussed in the literature. We propose nature-inspired metaheuristic algorithms, like particle swarm optimization (PSO) and its variants, to solve such optimization problems. We demonstrate that such techniques, which are easy to implement, can find different types of optimal designs for models with several factors efficiently. To facilitate use of such algorithms, we provide computer codes to generate tailor made optimal designs and evaluate efficiencies of competing designs. As applications, we apply PSO and find Bayesian optimal designs for Exponential models useful in HIV studies and re-design a car-refuelling study for a Logistic model with ten factors and some interacting factors.

中文翻译：

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基于粒子群算法的局部和贝叶斯D最优设计发现

当基于模型的方法合适时，最佳设计可以指导如何明智地收集数据，以最小的成本进行可靠的推理。但是，为具有几个可能相互作用的因素的统计模型找到最佳设计在理论和计算上都具有挑战性，并且在文献中很少讨论此问题。我们提出了自然启发式的元启发式算法，例如粒子群优化（PSO）及其变体，以解决此类优化问题。我们证明，这种技术易于实施，可以有效地为具有多个因素的模型找到不同类型的最优设计。为了促进此类算法的使用，我们提供了计算机代码以生成量身定制的最佳设计并评估竞争设计的效率。作为应用，