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A novel design of experiment algorithm using improved evolutionary multi-objective optimization strategy
Engineering Applications of Artificial Intelligence ( IF 8 ) Pub Date : 2021-05-07 , DOI: 10.1016/j.engappai.2021.104283
Yuhong Li , Ni Li , Guanghong Gong , Jin Yan

The paper aims to propose an intelligent design of experiment (DOE) algorithm using an improved evolutionary multi-objective optimization approach. Adaptive evolutionary strategies are embedded in the algorithm to support the design of simulation test schemes with multiple factors whose levels are same or different. Comparative results with several existing DOE algorithms show better sampling capacity and fine sampling efficiency of the proposed algorithm. Application effects of a complex flight simulator indicate the algorithm a wide technological prospect of serving well certain complex systems.



中文翻译:

改进的进化多目标优化策略的实验算法新颖设计

本文旨在提出一种使用改进的进化多目标优化方法的智能实验设计(DOE)算法。自适应进化策略被嵌入到算法中,以支持具有相同或不同级别的多个因素的模拟测试方案的设计。与几种现有DOE算法的比较结果表明,该算法具有更好的采样能力和优良的采样效率。复杂飞行模拟器的应用效果表明,该算法具有很好的技术前景,可以很好地服务于某些复杂系统。

更新日期:2021-05-07
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