start:hype_file_reference:optpar.txt
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start:hype_file_reference:optpar.txt [2018/09/06 15:59] cpers [File content] |
start:hype_file_reference:optpar.txt [2018/09/07 09:31] cpers [optpar.txt] |
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| Maximum 100 model parameters may be optimised simultaneously. To optimise more parameters, the code needs to be changed (set //maxoptpar// to a higher value). All parameters are described in the section on [[start:hype_file_reference:par.txt|par.txt]], but not all of them can be calibrated. The objective function of the optimization is defined in [[start:hype_file_reference:info.txt|info.txt]] as the combination of criteria chosen, see [[start:hype_file_reference:info.txt#performance_criteria_options|Performance criteria options]]. | Maximum 100 model parameters may be optimised simultaneously. To optimise more parameters, the code needs to be changed (set //maxoptpar// to a higher value). All parameters are described in the section on [[start:hype_file_reference:par.txt|par.txt]], but not all of them can be calibrated. The objective function of the optimization is defined in [[start:hype_file_reference:info.txt|info.txt]] as the combination of criteria chosen, see [[start:hype_file_reference:info.txt#performance_criteria_options|Performance criteria options]]. | ||
| - | There are eight methods of optimisation implemented in HYPE as detailed in the table below. Additionally, there are two other tasks for output generation, ''WA'' and ''WS'', which produce detailed performance and simulation results for all runs performed during optimisation. Tasks ''WA'' and ''WS'' are compatible with selected optimisation methods only, a denoted in the table. The task of organized scanning ''SC'' is a parameter investigation method. | + | There are eight methods of optimisation implemented in HYPE as detailed in the table below (read more about them in the [[start:hype_tutorials:automatic_calibration|tutorial]]). Additionally, there are two other tasks for output generation, ''WA'' and ''WS'', which produce detailed performance and simulation results for all runs performed during optimisation. Tasks ''WA'' and ''WS'' are compatible with selected optimisation methods only, a denoted in the table. The task of organized scanning ''SC'' is a parameter investigation method. |
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| |''DEMC_crossover''|//real//|1|crossover probability for DEMC method. Probability that the proposed candidate is chosen instead of the parent parameter. Large ''DEMC_crossover'' values mean larger probability that the proposal is chosen. Set to 1, all proposals are accepted. This makes it harder to find an acceptable overall proposal because all parameters are changed in every generation. Set to 0.5, each parameter candidate has only a 50% chance to be accepted into the next proposal.| | |''DEMC_crossover''|//real//|1|crossover probability for DEMC method. Probability that the proposed candidate is chosen instead of the parent parameter. Large ''DEMC_crossover'' values mean larger probability that the proposal is chosen. Set to 1, all proposals are accepted. This makes it harder to find an acceptable overall proposal because all parameters are changed in every generation. Set to 0.5, each parameter candidate has only a 50% chance to be accepted into the next proposal.| | ||
| |''DEMC_sigma''|//real//|0.1|sample error standard deviation for DEMC method. Base for the standard deviation of the random perturbation, which adds random noise to the proposed parameter in addition to the gamma-mutation. This value is multiplied with 3rd-row value for each parameter (see description of parameter rows below).| | |''DEMC_sigma''|//real//|0.1|sample error standard deviation for DEMC method. Base for the standard deviation of the random perturbation, which adds random noise to the proposed parameter in addition to the gamma-mutation. This value is multiplied with 3rd-row value for each parameter (see description of parameter rows below).| | ||
| - | |''DEMC_accprob''|//integer//|0|scaling factor for probabilistic acceptance for DEMC method (0 = off (default); >0 = on). If set to off, parameter proposals will only (and always) be accepted if the likelyhood score decreases (= better performance). If turned on, also proposals with higher likelyhood score can be accepted; better performance will give higher probability of acceptance. High values of the scaling factor will also increase the probability of acceptance.| | + | |''DEMC_accprob''|//integer//|0|scaling factor for probabilistic acceptance for DEMC method (0 = off (default); >0 = on). If set to off, parameter proposals will only (and always) be accepted if the objective function decreases (= better performance). If turned on, also proposals with higher value of the objective function can be accepted; better performance will give higher probability of acceptance. High values of the scaling factor will also increase the probability of acceptance.| |
| |''BR_diagStp''|//Y/N//|YES|flag for taking a diagonal step at the end of each iteration (BN method)| | |''BR_diagStp''|//Y/N//|YES|flag for taking a diagonal step at the end of each iteration (BN method)| | ||
| |''num_maxItr''|//integer//|500|max amount of iterations (interrupt non-MonteCarlo methods)| | |''num_maxItr''|//integer//|500|max amount of iterations (interrupt non-MonteCarlo methods)| | ||
start/hype_file_reference/optpar.txt.txt ยท Last modified: 2024/01/25 11:37 (external edit)
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