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

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...

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Vydáno v:Algorithms Ročník 2; číslo 1; s. 259 - 281
Hlavní autoři: Carnero, Mercedes, Hernández, José L., Sánchez, Mabel C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2009
Témata:
Population based incremental learning algorithms
Scatter search
Sensor location
Stochastic optimization
Tabu search
ISSN:1999-4893, 1999-4893
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1999-4893
1999-4893
DOI:10.3390/a2010259