| 1. |
- Dhak, Janice, et al.
(author)
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Developing a generic method for paper mill optimization
- 2004
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In: Proceedings of the PAPTAC Control Systems 2004 Conference, (Quebec City, Canada, June 14-18). ; , s. 207-214
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Conference paper (peer-reviewed)abstract
- A generic method for formulating pulp and paper optimization problems is presented. Two ongoing projects in the framework of the DOTS project illustrate the method: optimization of sizing quality at a specialty paper mill, and optimization of the water and broke systems at a coated paper mill. Explicit and implicit formulations are compared, and different usages of external simulators in conjunction with optimization are discussed. The problems are solved using MATLAB/TOMLAB. Some results from different optimization algorithms are also presented.
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| 2. |
- Dhak, Janice, et al.
(author)
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Generic methods for paper mill optimisation
- 2004
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In: PTS-COST Symposium Simulation and Process Control for the Paper Industry, (Munchen, Germany, March 9-10, 2004).
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Conference paper (peer-reviewed)
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| 3. |
- Fransson, C. -M, et al.
(author)
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Global controller optimization using horowitz bounds
- 2002
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In: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 247-252
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Conference paper (peer-reviewed)abstract
- A procedure for global optimization of PID type controller parameters for SISO plants with model uncertainty is presented. Robustness to the uncertainties is guaranteed by the use of Horowitz bounds, which are used as constraints when low frequency performance is optimized. The basic idea of both the optimization and the parameter tuning is to formulate separate criteria for low, mid and high frequency closed loop properties. The trade-off between stability margins, high frequency robustness and low frequency performance is then elucidated and, hence, the final choice of parameters is facilitated. The optimization problems are non-convex and ill-conditioned and we use a combination of new global and standard local optimization algorithms available in the TOMLAB optimization environment to solve the problem. The method does not rely on a good initial guess and converges fast and robustly. It is applied to a controller structure comparison for a plant with an uncertain mechanical resonance. For a given control activity and stability margin as well as identical tuning parameters it is shown that a PID controller achieves slightly improved low frequency performance compared to an H∞ controller based on loop-shaping. The reason for this somewhat surprising result is the roll-off in the H∞ controller, which adds additional high frequency robustness compared to the PID controller. Computationally, a factor of 10-20 has been gained compared to an earlier, less general, version of the procedure.
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| 4. |
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| 5. |
- Holmström, Kenneth, 1954-, et al.
(author)
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Adaptive Radial Basis Algorithms (ARBF) for Expensive Black-Box Global MINLP Optimization
- 2008
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In: SIOPT08 - Abstracts. ; , s. 132-
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Conference paper (peer-reviewed)abstract
- Improvements of the adaptive radial basis function algo-rithm (ARBF) for computationally costly optimization are presented. A new target value search algorithm and modifications to improve robustness and speed are discussed. The algoritm is implemented in solver ARBFMIP in the TOM-LAB Optimization Environment (http://tomopt.com/). Solvers in TOMLAB are used to solve global and local subproblems. Results and comparisons with other solvers are presented for global optimization test problems. Performance on costly real-life applications are reported.
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| 6. |
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| 7. |
- Holmström, Kenneth
(author)
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An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
- 2008
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In: Journal of Global Optimization. - : Springer Science and Business Media LLC. - 1573-2916 .- 0925-5001. ; 41:3, s. 447-464
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Journal article (peer-reviewed)abstract
- Powerful response surface methods based on kriging and radial basis function (RBF) interpolation have been developed for expensive, i.e. computationally costly, global nonconvex optimization. We have implemented some of these methods in the solvers rbfSolve and EGO in the TOMLAB Optimization Environment (http://www.tomopt.com/tomlab/). In this paper we study algorithms based on RBF interpolation. The practical performance of the RBF algorithm is sensitive to the initial experimental design, and to the static choice of target values. A new adaptive radial basis interpolation (ARBF) algorithm, suitable for parallel implementation, is presented. The algorithm is described in detail and its efficiency is analyzed on the standard test problem set of Dixon-Szego. Results show that it outperforms the published results of rbfSolve and several other solvers.
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| 8. |
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| 9. |
- Holmström, Kenneth
(author)
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An Adaptive Radial Basis Algorithm (ARBF) for Mixed-Integer Expensive Constrained Global Optimization
- 2005
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In: Proceedings of the International Workshop on Global Optimization. - : Universidad de Almería. ; , s. 133-140
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Conference paper (peer-reviewed)abstract
- A mixed-integer constrained extension of the radial basis function (RBF) interpolation algorithm for computationally costly global non-convex optimization is presented. Implementation in TOM-LAB (http://tomlab.biz) solver rbfSolve is discussed. The algorithm relies on mixed-integer nonlinear (MINLP) sub solvers in TOMLAB, e.g. OQNLP, MINLPBB or the constrained DIRECT solvers (glcDirect or glcSolve). Depending on the initial experimental design, the basic RBF algorithm sometimes fails and make no progress. A new method how to detect when there is a problem is presented. We discuss the causes and present a new faster and more robust Adaptive RBF (ARBF) algorithm. Test results for unconstrained problems are discussed.
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| 10. |
- Holmström, Kenneth, 1954-, et al.
(author)
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An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization
- 2008
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In: Optimization and Engineering. - : Springer US. - 1389-4420 .- 1573-2924. ; 9:4, s. 311-339
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Journal article (peer-reviewed)abstract
- Response surface methods based on kriging and radial basis function (RBF) interpolationhave been successfully applied to solve expensive, i.e. computationally costly,global black-box nonconvex optimization problems.In this paper we describe extensions of these methods to handle linear, nonlinear, and integer constraints. In particular, algorithms for standard RBF and the new adaptive RBF (ARBF) aredescribed. Note, however, while the objective function may be expensive, we assume that any nonlinear constraints are either inexpensive or are incorporated into the objective function via penalty terms. Test results are presented on standard test problems, both nonconvexproblems with linear and nonlinear constraints, and mixed-integernonlinear problems (MINLP). Solvers in the TOMLAB OptimizationEnvironment (http://tomopt.com/tomlab/) have been compared,specifically the three deterministic derivative-free solversrbfSolve, ARBFMIP and EGO with three derivative-based mixed-integernonlinear solvers, OQNLP, MINLPBB and MISQP, as well as the GENOsolver implementing a stochastic genetic algorithm. Results showthat the deterministic derivative-free methods compare well with thederivative-based ones, but the stochastic genetic algorithm solver isseveral orders of magnitude too slow for practical use.When the objective function for the test problems is costly to evaluate, the performance of the ARBF algorithm proves to be superior.
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