The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Ch'i-hsin Lin - One of the best experts on this subject based on the ideXlab platform.
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An ordinal Optimization Theory-based algorithm for solving the optimal power flow problem with discrete control variables
IEEE Transactions on Power Systems, 2004Co-Authors: Shin-yeu Lin, Ch'i-hsin LinAbstract:The optimal power flow (OPF) problem with discrete control variables is an NP-hard problem in its exact formulation. To cope with the immense computational-difficulty of this problem, we propose an ordinal Optimization Theory-based algorithm to solve for a good enough solution with high probability. Aiming for hard Optimization problems, the ordinal Optimization Theory, in contrast to heuristic methods, guarantee to provide a top n% solution among all with probability more than 0.95. The approach of our ordinal Optimization Theory-based algorithm consists of three stages. First, select heuristically a large set of candidate solutions. Then, use a simplified model to select a subset of most promising solutions. Finally, evaluate the candidate promising-solutions of the reduced subset using the exact model. We have demonstrated the computational efficiency of our algorithm and the quality of the obtained solution by comparing with the competing methods and the conventional approach through simulations.
Shieh-shing Lin - One of the best experts on this subject based on the ideXlab platform.
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An ordinal Optimization Theory-based algorithm for a class of simulation Optimization problems and application
Expert Systems with Applications, 2009Co-Authors: Shih-cheng Horng, Shieh-shing LinAbstract:In this paper, we have proposed an ordinal Optimization Theory-based two-stage algorithm to solve for a good enough solution of the stochastic simulation Optimization problem with huge input-variable space @Q. In the first stage, we construct a crude but effective model for the considered problem based on an artificial neural network. This crude model will then be used as a fitness function evaluation tool in a genetic algorithm to select N excellent settings from @Q. In the second stage, starting from the selected N excellent settings we proceed with the existing goal softening searching procedures to search for a good enough solution of the considered problem. We applied the proposed algorithm to the reduction of overkills and retests in a wafer probe testing process, which is formulated as a stochastic simulation Optimization problem that consists of a huge input-variable space formed by the vector of threshold values in the testing process. The vector of good enough threshold values obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency. We have also justified the performance of the proposed algorithm in a wafer probe testing process based on the ordinal Optimization Theory.
Baoding Liu - One of the best experts on this subject based on the ideXlab platform.
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Uncertain programming: a unifying Optimization Theory in various uncertain environments
Applied Mathematics and Computation, 2001Co-Authors: Baoding LiuAbstract:By uncertain programming we mean the Optimization Theory in generally uncertain (random, fuzzy, fuzzy random, grey, etc.) environments. Three broad classes of uncertain programming are expected value models and chance-constrained programming as well as dependent-chance programming. In order to solve general uncertain programming models, a simulation-based genetic algorithm is also documented. Finally, some applications and further research problems appearing in this area are posed.
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FUZZ-IEEE - Uncertain programming: Optimization Theory in uncertain environments
Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 1Co-Authors: Baoding LiuAbstract:By uncertain programming we mean the Optimization Theory in generally uncertain (random, fuzzy, fuzzy random, rough, etc.) environments. Stochastic programming, fuzzy programming, fuzzy random programming and rough programming are subtopics of uncertain programming. The paper provides a brief introduction to uncertain programming, including modeling ideas, hybrid intelligent algorithms, and applications in uncertain decision systems. Some further research problems appearing in this area are also posed.
Shin-yeu Lin - One of the best experts on this subject based on the ideXlab platform.
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An ordinal Optimization Theory-based algorithm for solving the optimal power flow problem with discrete control variables
IEEE Transactions on Power Systems, 2004Co-Authors: Shin-yeu Lin, Ch'i-hsin LinAbstract:The optimal power flow (OPF) problem with discrete control variables is an NP-hard problem in its exact formulation. To cope with the immense computational-difficulty of this problem, we propose an ordinal Optimization Theory-based algorithm to solve for a good enough solution with high probability. Aiming for hard Optimization problems, the ordinal Optimization Theory, in contrast to heuristic methods, guarantee to provide a top n% solution among all with probability more than 0.95. The approach of our ordinal Optimization Theory-based algorithm consists of three stages. First, select heuristically a large set of candidate solutions. Then, use a simplified model to select a subset of most promising solutions. Finally, evaluate the candidate promising-solutions of the reduced subset using the exact model. We have demonstrated the computational efficiency of our algorithm and the quality of the obtained solution by comparing with the competing methods and the conventional approach through simulations.
Shih-cheng Horng - One of the best experts on this subject based on the ideXlab platform.
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An ordinal Optimization Theory-based algorithm for a class of simulation Optimization problems and application
Expert Systems with Applications, 2009Co-Authors: Shih-cheng Horng, Shieh-shing LinAbstract:In this paper, we have proposed an ordinal Optimization Theory-based two-stage algorithm to solve for a good enough solution of the stochastic simulation Optimization problem with huge input-variable space @Q. In the first stage, we construct a crude but effective model for the considered problem based on an artificial neural network. This crude model will then be used as a fitness function evaluation tool in a genetic algorithm to select N excellent settings from @Q. In the second stage, starting from the selected N excellent settings we proceed with the existing goal softening searching procedures to search for a good enough solution of the considered problem. We applied the proposed algorithm to the reduction of overkills and retests in a wafer probe testing process, which is formulated as a stochastic simulation Optimization problem that consists of a huge input-variable space formed by the vector of threshold values in the testing process. The vector of good enough threshold values obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency. We have also justified the performance of the proposed algorithm in a wafer probe testing process based on the ordinal Optimization Theory.
