@article{LI201768,
title = "Fast source term estimation using the PGA-NM hybrid method",
journal = "Engineering Applications of Artificial Intelligence",
volume = "62",
pages = "68 - 79",
year = "2017",
issn = "0952-1976",
doi = "https://doi.org/10.1016/j.engappai.2017.03.010",
url = "http://www.sciencedirect.com/science/article/pii/S0952197617300647",
author = "Hui Li and Jianwen Zhang",
keywords = "Gaussian dispersion model, Pasquill-Gifford dispersion model, Nelder-Mead algorithm, Parallel genetic algorithm, Inversion problem",
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."
}