A novel design of experiment algorithm using improved evolutionary multi-objective optimization strategy. (June 2021)

• Record Type: Journal Article
• Authors: Li, Yuhong; Li, Ni; Gong, Guanghong; Yan, Jin
• Is Part Of: Engineering applications of artificial intelligence. Volume 102(2021)
• Publication Date: 2021-06
• Subjects: Design of Experiments -- Evolutionary multi-objective optimization -- Sampling capacity -- Sampling efficiency -- Complex systems; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104283
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.