Use of Discretization and Solution History in Stochastic Optimization

J. Lähteenmäki
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J. Lähteenmäki: Laboratory of Electromechanics, Helsinki University of Technology

A chapter in Optimization and Inverse Problems in Electromagnetism, 2003, pp 79-84 from Springer

Abstract: Abstract A practical improvement for stochastic algorithms used with numerical models is proposed. Combined use of solution space discretization and solution history is implemented. Discretization gives the possibility to use a solution history where all the solutions evaluated are stored. Use of the solution history guarantees that a single solution candidate is calculated only once. This approach is useful for the stochastic algorithms that typically evaluate many solution candidates The approach is most useful in the cases where a stochastic algorithm decreases the search space during the optimization process. The improvement proposed was tested in optimization of high-speed induction motors modeled with 2D finite element analysis software. With a genetic optimization algorithm, the average time saving for seven separate optimization runs was 39%.

Keywords: optimization; search space discretization; solution history (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-2494-4_9

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DOI: 10.1007/978-94-017-2494-4_9

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