Algorithms 2009, 2(1), 259-281; doi:10.3390/a2010259
Article

Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics

1email, 1email and 2,* email
Received: 3 November 2008; in revised form: 8 January 2009 / Accepted: 17 February 2009 / Published: 20 February 2009
(This article belongs to the Special Issue Sensor Algorithms)
Download PDF [244 KB, uploaded 20 February 2009]
Abstract: In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.
Keywords: Sensor location; Stochastic optimization; Tabu search; Scatter search; Population based incremental learning algorithms
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Carnero, M.; Hernández, J.L.; Sánchez, M.C. Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics. Algorithms 2009, 2, 259-281.

AMA Style

Carnero M, Hernández JL, Sánchez MC. Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics. Algorithms. 2009; 2(1):259-281.

Chicago/Turabian Style

Carnero, Mercedes; Hernández, José L.; Sánchez, Mabel C. 2009. "Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics." Algorithms 2, no. 1: 259-281.

Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert