New efficient breadth‐first/level traversal tree search method for the design and upgrade of sensor networks

Nguyen, Duy Quang; Bagajewicz, Miguel J.

doi:10.1002/aic.12332

Cited by 14 publications

(20 citation statements)

References 21 publications

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“…These problems include a revenue based term in the objective function. For such problems, the tree search algorithm loses its guarantee of global optimality. However, it is expected that the MICP version of these problems will yield global optimality and receive similar computational benefits from a GBD solution procedure.…”

Section: Resultsmentioning

confidence: 99%

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Covariance‐based hardware selection part IV: Solution using the generalized benders decomposition

2016

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Recently the covariance based hardware selection problem has been shown to be of the mixed integer convex programming (MICP) class. While such a formulation provides a route to global optimality, use of the branch and bound search procedure has limited application to fairly small systems. The particular bottleneck is that during each iteration of the branch and bound search, a fairly slow semi-definite programming (SDP) problem must be solved to its global optimum. In this work, we illustrate that a simple reformulation of the MICP and subsequent application of the generalized Benders decomposition algorithm will result in massive reductions in computational effort. While the resulting algorithm must solve multiple mixed integer linear programs, this increase in computational effort is significantly outweighed by the reduction in the number of SDP problems that must be solved.

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Section: Resultsmentioning

confidence: 99%

“…Since then a number of reformulations and solution procedures have been proposed to improve computational efficiency. The depth‐first tree search algorithm was extended to a breadth‐first/level‐traversal scheme . This method improved efficiency, but only for cases in which feasible solutions were found on the left side of the tree.…”

Section: Introductionmentioning

confidence: 99%

Covariance‐based hardware selection part IV: Solution using the generalized benders decomposition

2016

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“…SNDP was formulated by Bagajewicz [1], and tackled by Nguyen and Bagajewicz [2] using a new tree search method that exploits certain cost properties of the different nodes in the tree to efficiently prune non optimal nodes using a breadth-first/level traversal tree search method to obtain the global optimum. Other approaches modeled the problem combining the integer and non-linear programming and solved it by means of depth-first or breadth-first tree searches.…”

Section: Introductionmentioning

confidence: 99%

Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design

Minetti

Hernández

Carnero

et al. 2020

JC&ST

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Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We are researching on the adjustments of its control parameters to obtain the best HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.

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“…Its solution has been tackle using exact and stochastic algorithms. Comprehensive reviews of these methods can be found elsewere. − …”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms

Carnero

Hernández

Sánchez

2018

Ind. Eng. Chem. Res.

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The optimal selection of sensor structures improves the knowledge of the current plant state, which is a central issue for the decision-making process. Instrumentation design is a challenging optimization problem that involves a huge amount of binary variables that represent the possible sensor locations. In this work, the limitations of the current design strategies are discussed, and they support the application of evolutionary solution methods. Among them, the estimation of distribution algorithms (EDAs) arises as a convenient alternative to solving the problem. These are stochastic optimization strategies devised to capture complex interactions among problem variables by learning the probabilistic model of candidate solutions and its sampling to generate the next population. From the broad spectrum of EDAs that use multivariate models, two representative procedures are selected that significantly differ in the methods used for learning and sampling those models. Furthermore, a comparative performance study is conducted to evaluate the benefits of increasing the complexity of the distribution model with respect to a memetic procedure based on univariate models.

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New efficient breadth‐first/level traversal tree search method for the design and upgrade of sensor networks

Cited by 14 publications

References 21 publications

Covariance‐based hardware selection part IV: Solution using the generalized benders decomposition

Covariance‐based hardware selection part IV: Solution using the generalized benders decomposition

Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design

Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms

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