Academic Journal

Surrogate Strategies for Computationally Expensive Optimization Problems with CPU-Time Correlated Functions

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Surrogate Strategies for Computationally Expensive Optimization Problems with CPU-Time Correlated Functions
Συγγραφείς: Magallanez, Raymond, Jr.
Πηγή: Theses and Dissertations
Στοιχεία εκδότη: AFIT Scholar
Έτος έκδοσης: 2007
Συλλογή: AFTI Scholar (Air Force Institute of Technology)
Θεματικοί όροι: Mathematical optimization--Computer programs, Computer algorithms, Numerical Analysis and Computation
Περιγραφή: This research focuses on numerically solving a class of computationally expensive optimization problems that possesses a unique characteristic: as the optimal solution is approached, the computational time required to compute an objective function value decreases. This is motivated by an application in which each objective function evaluation requires both a numerical fluid dynamics simulation and an image registration and comparison process. The goal is to find the parameters of a predetermined image by comparing the flow dynamics from the numerical simulation and the predetermined image through the image comparison process. The generalized pattern search and mesh adaptive direct search methods were applied in a way that employs surrogate functions in the search step to reduce the number of costly function evaluations. The surrogate functions are formed, based on either previous function values or their computational times, or both. The solution to the surrogate optimization problem can be solved easily and provides an improved solution quickly. A time cut-off parameter was also added to the objective function to allow its termination during the comparison process if the computational time exceeded a specified threshold. The approach was tested on two problems using the NOMADm and DACE MATLAB® software packages, and results are presented.
Τύπος εγγράφου: text
Περιγραφή αρχείου: application/pdf
Γλώσσα: unknown
Relation: https://scholar.afit.edu/etd/2926; https://scholar.afit.edu/context/etd/article/3927/viewcontent/AFIT_GOR_ENC_07_01.pdf
Διαθεσιμότητα: https://scholar.afit.edu/etd/2926
https://scholar.afit.edu/context/etd/article/3927/viewcontent/AFIT_GOR_ENC_07_01.pdf
Αριθμός Καταχώρησης: edsbas.719BC697
Βάση Δεδομένων: BASE
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IllustrationInfo
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  Data: Surrogate Strategies for Computationally Expensive Optimization Problems with CPU-Time Correlated Functions
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Magallanez%2C+Raymond%2C+Jr%2E%22">Magallanez, Raymond, Jr.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Theses and Dissertations
– Name: Publisher
  Label: Publisher Information
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  Data: AFIT Scholar
– Name: DatePubCY
  Label: Publication Year
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  Data: 2007
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  Data: AFTI Scholar (Air Force Institute of Technology)
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  Data: <searchLink fieldCode="DE" term="%22Mathematical+optimization--Computer+programs%22">Mathematical optimization--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+algorithms%22">Computer algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+Analysis+and+Computation%22">Numerical Analysis and Computation</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This research focuses on numerically solving a class of computationally expensive optimization problems that possesses a unique characteristic: as the optimal solution is approached, the computational time required to compute an objective function value decreases. This is motivated by an application in which each objective function evaluation requires both a numerical fluid dynamics simulation and an image registration and comparison process. The goal is to find the parameters of a predetermined image by comparing the flow dynamics from the numerical simulation and the predetermined image through the image comparison process. The generalized pattern search and mesh adaptive direct search methods were applied in a way that employs surrogate functions in the search step to reduce the number of costly function evaluations. The surrogate functions are formed, based on either previous function values or their computational times, or both. The solution to the surrogate optimization problem can be solved easily and provides an improved solution quickly. A time cut-off parameter was also added to the objective function to allow its termination during the comparison process if the computational time exceeded a specified threshold. The approach was tested on two problems using the NOMADm and DACE MATLAB® software packages, and results are presented.
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  Data: https://scholar.afit.edu/etd/2926; https://scholar.afit.edu/context/etd/article/3927/viewcontent/AFIT_GOR_ENC_07_01.pdf
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    Subjects:
      – SubjectFull: Mathematical optimization--Computer programs
        Type: general
      – SubjectFull: Computer algorithms
        Type: general
      – SubjectFull: Numerical Analysis and Computation
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