A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization

Report No. ARL-TR-6928
Authors: David A. Richie, James A. Ross, Song J. Park, and Dale R. Shires
Date/Pages: May 2014; 22 pages
Abstract: Determining the positioning of assets within an unfriendly urban environment subject to complex constraints presents a non-trivial geometric optimization problem. Within realistic scenarios, the risk and success metrics must generally be defined numerically and will lack simple closed-form representations. Moreover, in the case of non-separable objective functions that depend upon correlated positioning of individual assets, the state-space of the system will be of high dimension, requiring computationally intensive algorithms for optimization. This report presents a method developed for solving such systems using a Monte Carlo simulation technique for multi-objective correlated geometric optimization. Once line-of-sight via ray tracing approach is calculated, our algorithmperforms a Monte Carlo optimization to provide geospatial intelligence on entity placement using OpenCL framework. The solutions for optimal positioning, calculated through evaluating risk and success objective function with Markov chain Monte Carlo sampling, are presented graphically in this report.
Distribution: Approved for public release
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Last Update / Reviewed: May 1, 2014