Fast source term estimation using the PGA-NM hybrid method. (June 2017)

• Record Type: Journal Article
• Authors: Li, Hui; Zhang, Jianwen
• Is Part Of: Engineering applications of artificial intelligence. Volume 62(2017:Feb.)
• Page Start: 68
• Page End: 79
• Publication Date: 2017-06
• Subjects: Gaussian dispersion model -- Pasquill-Gifford dispersion model -- Nelder-Mead algorithm -- Parallel genetic algorithm -- Inversion problem; 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.2017.03.010
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: There are significant challenges related to estimating the source term of the atmospheric release. Urged on by robots in performing emergency responding tasks, a fast and accurate algorithm for this inversion problem is indispensable. Sometimes the NM simplex algorithm is efficient in the optimization problem, but sometimes the quality of convergence is unacceptable as a numerical breakdown, even for smooth and well-behaved functions. In contrast, full convergence might be seen in parallel genetic algorithms with a comparative slower convergence. In this paper we combine the PGA and the NM simplex algorithm by initializing simplex from the final individual of PGA results and obtaining the best vertex through simplex algorithm thereafter. A numerical simulation of the proposed algorithm shows noteworthy improvement of efficiency and robustness, compared with the PGA or the NM algorithm only. Abstract : Highlights: We shortened the computation time running on an embedded system to less than half a second that meets the need to perform emergency tasks. The algorithm is tolerant of the measurement errors. We combined the PGA and NM algorithm which was not found in other papers yet. The PGA-NM hybrid algorithm could be developed and applied to other optimization problems in a similar way.