2019
DOI: 10.1016/j.pepi.2019.05.001
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Selenophysical parameter inversion in the Lunar Southern Hemisphere Highland based on mutant particle swarm optimization

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Cited by 3 publications
(2 citation statements)
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“…It is important to note that Equation ( 8) is nonlinear, which can make parameter estimation challenging. The particle swarm optimization (PSO) method simulates the social behavior of birds, bees, or schools of fish, and performs well in solving nonlinear problems with multiple parameters [32]. We considered the PSO method to estimate the optimized parameters of Phobos.…”
Section: Theory and Methodsmentioning
confidence: 99%
“…It is important to note that Equation ( 8) is nonlinear, which can make parameter estimation challenging. The particle swarm optimization (PSO) method simulates the social behavior of birds, bees, or schools of fish, and performs well in solving nonlinear problems with multiple parameters [32]. We considered the PSO method to estimate the optimized parameters of Phobos.…”
Section: Theory and Methodsmentioning
confidence: 99%
“…Geometrically, the lunar crust is bounded by the high-resolution LOLA topography (Smith et al, 2010) and a crust-mantle interface (Wieczorek et al, 2013). Several regional (Liang et al, 2014;Jansen et al, 2017;Zhang et al, 2018;Zhao et al, 2019;Zhong et al, 2019) or global (Wieczorek et al, 2013;Han, 2013;Besserer et al, 2014;Wahl and Oberst, 2019) density maps have already been provided from LOLA and GRAIL. Here, we calculate independent bulk density for the lunar crust in three types of function: 1) a constant, 2) laterally variable, and 3) 3D spatially variable (assuming a linear change in the radial direction).…”
Section: Introductionmentioning
confidence: 99%