Statistical mechanics-inspired optimization for sensor field reconfiguration

In a multi-objective optimization scenario (e.g., optimal sensor deployment and sensor field reconfiguration for detection of moving targets), the non-dominated points are usually concentrated within a small region of the large-dimensional decision space. This paper attempts to capture the low-dimensional behavior across the Pareto front by statistical mechanics-inspired optimization tools. A location-dependent energy function has been constructed and evaluated in terms of intensive temperature-like parameters in the sense of statistical mechanics. This low-order representation has been shown to permit rapid optimization of sensor field distribution on a simulation model of undersea operations.