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) |
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| Title of host publication | Proceedings of the 2010 American Control Conference, ACC 2010 |
| Pages | 714-719 |
| Number of pages | 6 |
| State | Published - Oct 15 2010 |
| Event | 2010 American Control Conference, ACC 2010 - Baltimore, MD, United States Duration: Jun 30 2010 → Jul 2 2010 |
Publication series
| Name | Proceedings of the 2010 American Control Conference, ACC 2010 |
|---|
Other
| Other | 2010 American Control Conference, ACC 2010 |
|---|---|
| Country | United States |
| City | Baltimore, MD |
| Period | 6/30/10 → 7/2/10 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
Cite this
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Statistical mechanics-inspired optimization for sensor field reconfiguration. / Mukherjee, Kushal; Gupta, Shalabh; Ray, Asok; Wettergren, Thomas A.
Proceedings of the 2010 American Control Conference, ACC 2010. 2010. p. 714-719 5530900 (Proceedings of the 2010 American Control Conference, ACC 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Statistical mechanics-inspired optimization for sensor field reconfiguration
AU - Mukherjee, Kushal
AU - Gupta, Shalabh
AU - Ray, Asok
AU - Wettergren, Thomas A.
PY - 2010/10/15
Y1 - 2010/10/15
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=77957778883&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957778883&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77957778883
SN - 9781424474264
T3 - Proceedings of the 2010 American Control Conference, ACC 2010
SP - 714
EP - 719
BT - Proceedings of the 2010 American Control Conference, ACC 2010
ER -