Openmdao Driver
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Other useful packages and applications not currently available on this page. AIDA implements the Adaptive Image Deconvolution Algorithm. Alglib is a cross-platform.
The OpenMDAO standard library contains an assortment of useful plugins to the framework sorted into four catgories: components, drivers, factories, and traits.
The basic interface conforms to OpenMDAO s driver API, which is discussed in The Driver API, and covers how to assign design variables, constraints, and.
Oct 07, 2014 OpenMDAO. OpenMDAO, designed by NASA Glenn Research Center, is an open-source software package built in the Python programming language that.
Drivers
Here, the fundamental OpenMDAO component classes Component and Assembly are loaded from openmdao.main, along with the CONMIN driver from.
Efficiency a virtue is the child of laziness and greed both vices, while much of our economic activity is devoted to preventing boredom in the idle time created.
The Hyperloop is a conceptual high-speed transportation system put forward by entrepreneur Elon Musk, 1 2 incorporating reduced-pressure tubes in which.
Other useful packages and applications not currently available on this page. AIDA implements the Adaptive Image Deconvolution Algorithm. Alglib, a cross-platform.
Genetic is a driver which performs optimization using a genetic algorithm based
on Pyevolve. Genetic is a global optimizer and
is ideal for optimizing problems with integer or discrete design variables because it
is a non-derivative based optimization method.
from openmdao.lib.api import Genetic
Design Variables
IOtraits are added to Genetic and become optimization parameters. Genetic will vary the set of
parameters to search for an optimum. Genetic supports three variable types:
Float, Int, and Enum. These types can be used as parameters in any
optimization.
You add design variables to Genetic using the add_parameter method.
from openmdao.main.api import Assembly,Component, set_as_top
from openmdao.lib.api import Float,Int,Enum
class SomeComp Component :
Arbitrary component with a few variables, but which does not really do
any calculations
w Float 0.0, low -10, high 10, iotype in
x Float 0.0, low 0.0, high 100.0, iotype in
y Int 10, low 10, high 100, iotype in
z Enum -10, -5, 0, 7, iotype in
class Simulation Assembly :
Top Level Assembly used for simulation
def __init__ self :
Adds the Genetic driver to the assembly
super Simulation,self. __init__
self.add driver, Genetic
self.add comp, SomeComp
Driver process definition
self.driver.workflow.add self.comp
self.driver.add_parameter comp.x
self.driver.add_parameter comp.y
self.driver.add_parameter comp.z
top Simulation
set_as_top top
In the above example, three parameters were added to the optimizer. The optimizer
figures out for itself what type of variable it is and behaves appropriately. In all three
cases, since no low or high arguments were provided, the optimizer will use the values
from the metadata provided in the variable deceleration.
For comp.x the optimizer will try floats between 0.0 and 100.0. For comp.y the optimizer
will try integers between 10 and 100. For comp.z the optimizer will pick from
the list of allowed values: -10,-5,0,7.
You can override the low and high values from the metadata if you want
the optimizer to use a different range instead of the default.
top.driver.add_parameter comp.w, low 5.0, high 7.0
Now, for comp.x the optimizer will only try values between 5.0 and 7.0. Note that low and high
are applicable only to Float and Int variables. For Enum variables, low and high
are not applicable.
Configuration
When setting the objective you can specify a single
variable name or a more complex function, such as
top.driver.add_objective comp.x
top.driver.add_objective 2 comp.x comp.y 3 comp.z
In the second example above, a more complex objective function was created where the overall objective was
a weighted combination of comp.x, comp.y, and comp.z.
To set the optimizer to either minimize or maximize your objective, you set the
opt_type variable of Genetic to minimize or maximize.
top.driver.opt_type minimize
You can control the size of the population in each generation and the maximum number of generations in
your optimization with the population_size and generations variables.
top.driver.population_size 80
top.driver.generations 100
As you increase the population size, you are effectively adding diversity in to the gene pool of your
optimization. A large population means that a larger number of individuals from a given generation will
be chosen to provide genetic material for the next generation. So there is a better chance that weaker individuals
will pass on their genes. This diversity helps to ensure that your optimization will
find a true global optimum within the allowed design space. However, it also serves to slow down the
optimization because of the increased number of function evaluations necessary for each generation.
Picking an appropriate value for the maximum number of generations will depend highly on the specifics of
your problem. Setting this number too low will likely prevent the optimization from converging on a true
optimum. Setting it too high will help you find the true optimum, but you may end up wasting the computation
time on later generations where the optimum has been found.
You can further control the behavior of the genetic algorithm by setting the crossover_rate,
mutation_rate, selection_method, and elitism variables. These settings will allow you to
fine-tune the convergence of your optimization to achieve the desired result; however, for many
optimizations the default values will work well and won t need to be changed.
The crossover_rate controls the rate at which the crossover operator gets applied to the genome of a set of
individuals who are reproducing. The allowed values are between 0.0 and 1.0. A higher rate will mean that more of
the genes are swapped between parents. The result will be a more uniform population and better searching of the
design space. If the rate is set too high, then it is likely that stronger individuals could be lost to churn.
top.driver.crossover_rate 0.9
The mutation_rate controls how likely any particular gene is to experience a mutation. A low, but non-zero,
mutation rate will help prevent stagnation in the gene pool by randomly moving the values of genes. If this
rate is set too high, the algorithm basically degrades into a random search through the design space. The
allowed values are between 0.0 and 1.0.
top.driver.mutation_rate . 02
In a pure genetic algorithm, it is possible that your best performing individual will not survive from one
generation to the next due to competition, mutation, and crossover. If you want to ensure that the best
individual survives intact from one generation to the next, then turn on the elitism flag for your
optimization. This will ensure that the best individual is always copied to the next generation no matter
what.
top.driver.elitism True
A number of different commonly used selection algorithms are available. The default algorithm is the Roulette
Wheel Algorithm, but Tournament Selection, Rank Selection, and Uniform Selection are also available. The
selection_method variable allows you to select the algorithm; allowed values are: roulette_wheel,
tournament, rank, and uniform.
See the source documentation for more information on Genetic.
top.driver.selection_method rank.
