Statistical mechanics-inspired optimization for sensor field reconfiguration

Kushal Mukherjee; Shalabh Gupta; Asok Ray; Thomas A. Wettergren

doi:10.1109/acc.2010.5530900

Statistical mechanics-inspired optimization for sensor field reconfiguration

Kushal Mukherjee, Shalabh Gupta, Asok Ray, Thomas A. Wettergren

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the 2010 American Control Conference, ACC 2010
Publisher	IEEE Computer Society
Pages	714-719
Number of pages	6
ISBN (Print)	9781424474264
DOIs	https://doi.org/10.1109/acc.2010.5530900
State	Published - 2010

Publication series

Name	Proceedings of the 2010 American Control Conference, ACC 2010

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

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10.1109/acc.2010.5530900

Cite this

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title = "Statistical mechanics-inspired optimization for sensor field reconfiguration",

abstract = "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.",

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Statistical mechanics-inspired optimization for sensor field reconfiguration. / Mukherjee, Kushal; Gupta, Shalabh; Ray, Asok et al.

Proceedings of the 2010 American Control Conference, ACC 2010. IEEE Computer Society, 2010. p. 714-719 5530900 (Proceedings of the 2010 American Control Conference, ACC 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Ray, Asok

AU - Wettergren, Thomas A.

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AB - 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.

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Statistical mechanics-inspired optimization for sensor field reconfiguration

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