In this study we address existing deficiencies in the literature on applications of Particle Swarm Optimization to generate optimal designs. We present the results of a large computer study in which we bench-mark both efficiency and efficacy of PSO to generate high quality candidate designs for small-exact response surface scenarios commonly encountered by industrial practitioners. A preferred version of PSO is demonstrated and recommended. Further, in contrast to popular local optimizers such as the coordinate exchange, PSO is demonstrated to, even in a single run, generate highly efficient designs with large probability at small computing cost. Therefore, it appears beneficial for more practitioners to adopt and use PSO as tool for generating candidate experimental designs.
翻译:在这项研究中,我们讨论了关于应用粒子蒸汽优化以产生最佳设计方面的文献中存在的缺陷;我们介绍了一项大型计算机研究的结果,在这项研究中,我们从效率和效能两方面考虑确定PSO的效率和效力,以便为工业从业人员通常遇到的小型反应表面情景产生高质量的候选设计;演示并推荐了PSO的首选版本;此外,与协调交换等流行的当地优化工具相比,PSO被证明即使在单一情况下也能够产生极有可能以低成本计算的高效率设计;因此,看来更多的从业人员采用和使用PSO作为生成候选实验设计的工具是有益的。