DAKOTA Coliny Solis-Wets (SW)

Description
The Solis-Wets is a heuristic local search algorithm for continuous design variables. Solis Wets generates trial points using a multivariate normal distribution, and unsuccessful trial points are reflected about the current point to find a descent direction. This is a non-derivative optimization algorithm and constraint violations are handled by a simple penalty function.

References
DAKOTA Version 5.0 Reference Manual

ConstantPenalty:ConstantPenalty if set to true would implement simple weighted penalty function otherwise ConstraintPenalty is used.

ConstraintPenalty: The ConstraintPenalty is adapted to the value ConstraintPenalty/L, where L is the smallest step length used so far. Thus option must have a positive real value.

ContractAfterFailure: Specifies the number of unsuccessful cycles which must occur with a specific delta prior to contraction of the delta. Thus the algorithm does not immediately contract search step after one step and may be more likely to find a global minima. Option accepts a positive integer value.

ContractionFactor: In general, pattern search methods can expand and contract their step lengths. Coliny Solis-Wets algorithm contracts the step length by the value ContractionFactor, and expands the step length by the value (1/ContractionFactor). This depends on ExpandAfterSuccess option for the number of successful improvements before expansion of step length. In case of unsuccessful improvements the step length would have to be contracted immediately. The option accepts values in the range (0, 1).

ExpandAfterSuccess: Specifies how many successful objective function improvements must occur with a specific step length prior to expansion of the step length. Thus the algorithm ensures that it is on an improvement path before increasing step length immediately. Option accepts a positive integer value.

InitialDelta: Solis-Wets algorithm uses a set of offsets from the current iterate to locate improved points in the design space. Initial Delta provides the value for the initial step length. This option should have a positive real value.

Seed: Random seed is used to the algorithm to generate trial points. Same seed will help replicate results if other parameters stay the same. The option must have a positive integer greater than 1.

ThresholdDelta: This is the threshold size of the step length at which the algorithm can terminate. Reducing the value of this option may lead to better answers but could increase the function evaluations and optimization effort. ThresholdDelta must have a positive real value.

ConvergenceTolerance: The convergence tolerance specification provides a real value for controlling the termination of iteration. This option defines the threshold value on relative change in the objective function that indicates convergence for the problem. Value must be greater than zero for all cases.

MaxFunctionEvaluations: Function evaluation is the call to Analyzer to evaluate the objective function at the specified points. The maximum number of function evaluations is an integer limit for evaluations that the algorithm can attain. Algorithm can terminate with this criterion if no other criteria are satisfied. A single iteration can contain multiple function evaluations. MaxFunctionEvaluations must be a positive integer value.

MaxIterations: A single iteration will have multiple offset evaluations. This is the integer limit on number of iterations the algorithm can actually run. Option must have a positive integer value.

Output: This option controls the level of verbosity of messages user can receive from DAKOTA. The options go from Silent to Debug with increasing amount of messages returned from the infrastructure. View Output > Details should show the messages from DAKOTA infrastructure.

IntermediateFilesPath: User can specify the location where the intermediate files of optimization should be generated. By default files are written to the user's temporary directory.

TabularGraphicsData: Turning this option to true generates a file named dakota_tabular in IntermediateFilesPath directory. This file has the values of design variables, constraints and objective function for each evaluation stored in a tabular format.