Alternate Run Modes:Effective and Efficient Stochastic Global Optimization

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Effective and Efficient Stochastic Global Optimization - Multilevel Single Linkage

As previously mentioned, this GSSHA alternate run mode employs the independent ERDC implementation of the stochastic global optimization method Multilevel Single Linkage (MLSL), which employs the independent ERDC implementations of the LM/SLM local search methods for local searches. Please see the technical report ERDC-CHL-TR-12-2, and its related appendix material and example problem files, for a discussion of the MLSL method and also four examples that explain in a clear and practical way how to calibrate a (GSSHA) model in a model independent manner using the MLSL method. Prior to execution of this alternate GSSHA run mode, which employs the MLSL method, in addition to the activities that must be performed that uniformly apply to any of the four alternate GSSHA run modes (previously discussed in section 18.6), one must also prepare a file named mlsl.in (for “multilevel single linkage input”). The file named mlsl.in contains eleven entries, each specified on its own individual line. The eleven entries are (1) the MLSL parameter N, (2) the MLSL parameter γ, (3) the MLSL parameter σ, (4) the input parameter d2, which is a floating point value specifying a distance threshold that is used for comparison during execution of our implementation of MLSL; in particular, if during a given local search with MLSL, the distance computed between the current location in adjustable model parameter space and any of the previously computed parameter upgrade vectors, obtained either during the existing or with any of the previously performed local searches, is less than this specified threshold value, then the current local search is prematurely terminated with the assumption that it has progressed into a region of attraction of a previously visited local minimum - the impact of this input entry has not been explored with much detail, and it is often set to zero, (5) a character that is either ‘Y’ or ‘N’ to indicate whether the program will write (‘Y’) or read (‘N’) the file named “RandomNumberSeeds.prn” (more often than not this entry will by ‘Y’), (6) the initial seed that is an integer between 0 and 4,294,967,295, (7) the maximum number of MLSL iterations to perform, (8) the maximum number of local searches to perform, (9) the maximum number of local searches to perform with no improvement, (10) the maximum number of MLSL iterations to perform with no improvement, and (11) the objective function improvement fraction judged to be negligible. Please refer to the technical report ERDC-CHL-TR-12-2, and its related appendix material and example problem files, for a discussion of the previously mentioned MLSL input parameters, and via the examples, implicit guidance regarding potential values to specify for your given application. The required syntax to use this alternate GSSHA run mode for model calibration is:

gssha –mlsl case.pst

where case.pst is the modified control file. Please see the technical report ERDC-CHL-TR-12-2, and its related appendix material and example problem files, for a discussion of the MLSL method outputs, the primary model output being named “slm_chl_mlsl.rec”.

Example problem files (both input and output) associated with a Multilevel Single Linkage (MLSL) supervised GSSHA alternate run mode model calibration run, which are supplied for use as a go by


GSSHA User's Manual

18 Alternate Run Modes
18.1     MPI and OpenMP Parallelization
18.2     Simulation Setup for Alternate Run Modes
18.3     Batch Mode Runs
18.4     Automated Calibration with Shuffled Complex Evolution
18.5     Monte Carlo Runs
18.6     ERDC Automated Model Calibration Software
   18.6.1     Efficient Local Search
   18.6.2     Multistart
   18.6.3     Trajectory Repulsion
   18.6.4     Effective and Efficient Stochastic Global Optimization