STK ANALYZER
Analyzer is integrated
into the STK workflow
to help you automate
and analyze STK tradestudies in order tobetter understand thedesign of your system.
Analyzer is a fully integrated,
STK add-on module that:•	Blends engineering analysis capabilities
of Phoenix Integrations, Inc.’sModelCenter® with STK.
•	Enables you to perform analyses inSTK easily without involving
programming or scripting.
•	Introduces trade study andpost-processing capabilities.
•	Can be used with all STK
scenarios, including thosewith STK Astrogator satellites.

Analyzer analysis tools
Parametric study. Varies one item over arange to study the effects on various figuresof merit defining performance.
Carpet plot. Varies two items overranges to study the resultant behaviorof another parameter.
Design of experiments. Varies multiple
parameters creating a table of runs using
various design type algorithms to study theeffects on various parameters.
Probabilistic analysis. Runs studies in Monte
Carlo fashion. Output data can be analyzed
and presented in histogram fashion.
Optimizer. A separate extension thatprovides additional optimization
algorithms to Analyzer.

Optimizer
The Optimizer extension for STK
Analyzer extends Analyzer’s parametricanalysis capabilities:•	Adds three different optimization
algorithms to the four existing parametricanalyses offered with Analyzer.

•	Features two design tools for further
trade space exploration and parametricanalysis insight.

Gradient-based optimizer
Local search for optima in problems withsmooth, continuously varying objective andconstraint functions.

Variable Influence profiler
The Variable Influence profiler enables you
to gain a better understanding of variable
relationships and overall design trends.
• Performs sensitivity analysis on a scenario.
•	Encourages a more effective design
problem formation and location.

Adaptive Surrogate optimizer•	Provides more accurate
optimization results.

Part of Boeing’s Design Explorer software,
the Adaptive Surrogate Optimizer solves
complex problems characterized by longrunning analyses.

•	Generates variable importance plots that
illustrate the relative impact of selectedparameters on a scenario.

•	Intelligently uses non-physics-based
mathematical models (Kriging models)
to reduce the number of required
scenario executions.
•	As a global search algorithm, it is not likely
to get stuck in local optima.
•	Performs well in noisy design spaces and isrobust in the face of scenario failures.

Darwin Genetic optimizer
Darwin Genetic algorithms are ideally suitedfor design problems with discretely valued
design variables (e.g., integer variables).
Because they do not require objective or
constraint gradient information, genetic
algorithms effectively search discontinuousand “noisy” design spaces.

•	Generates main and interaction effects
plots that significantly reduces thecomplexity of the design task.

Prediction profiler
Interactively explores the design space,enabling you to.
•	Efficiently predict output variable values
for any combination of input variables.
•	Visualize slices of the resulting /n/dimensional design space (including
the effects of constraints).
•	Manually explore and search thedesign space for good designs.
This tool can be used to locate good designs,
find good starting points for a more formal
optimization algorithm, or visualize thedesign space around an optimum point
generated by an optimization algorithm.
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