A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization
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 algorithm performs 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2014
- Accession Number
- ADA603830
Entities
People
- Dale R. Shires
- David A. Richie
- James A. Ross
- Song J. Park
Organizations
- United States Army Research Laboratory