def __init__(self, dat=None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "PSUADE"
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p><b>PSUADE Optimiation Library</b></p></html>")
self.available = True
self.description = "PSUADE Optimzation solvers"
self.mp = False #can be parallel?
self.mobj = False #handles multiobjective?
self.minVars = 2 #minimum number of decision variables
self.maxVars = 10000 #max variables
self.options.add(
name='upper',
default=10.0,
dtype=float, #don't need this if default is proper type
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(name="tol",
default=None,
desc="Some termination crieria",
dtype=float)
self.options.add(name="Results name",
default="Opt_PSUADE",
desc="The name assigned to the flowsheet result set.")
def __init__(self, dat = None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "SciPy-SLSQP"
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p>Developer: Dieter Kraft</p>"
"<p>Algorithm Type: Han–Powell quasi–Newton method</p>"
"<p>Optimization Problems handling Capability: Nonlinear Optimization Problems with general equality & inequality constraints, and variable bounds.</p>"
"</html>")
self.options.add(
name='upper',
default=10.0,
dtype=float,
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(
name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(
name="ftol",
default=1.0e-9,
desc="Function abs tolerance termiantion condition",
dtype=float)
self.options.add(
name="eps",
default=1.0e-11,
desc="Jacobian approximation step size",
dtype=float)
self.options.add(
name="maxiter",
default=1000000,
desc="maximum number of iterations",
dtype=int)
self.options.add(
name="maxtime",
default=48.0,
desc="maximum time to allow for optimization (hours)",
dtype=float)
self.options.add(
name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(
name='Set Name',
default="SciPy-SLSQP",
dtype=str,
desc="Name of flowsheet result set to store data")
def __init__(self, dat = None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "SciPy-BFGS"
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p><b>SciPy BFGS</b></p>"
"</html>")
self.options.add(
name='upper',
default=10.0,
dtype=float,
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(
name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(
name="ftol",
default=1.0e-9,
desc="Function abs tolerance termiantion condition",
dtype=float)
self.options.add(
name="eps",
default=1.0e-11,
desc="Jacobian approximation step size",
dtype=float)
self.options.add(
name="maxeval",
default=1000000,
desc="maximum number of objective function evaluations",
dtype=int)
self.options.add(
name="maxtime",
default=48.0,
desc="maximum time to allow for optimization (hours)",
dtype=float)
self.options.add(
name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(
name='Set Name',
default="SciPy-BFGS",
dtype=str,
desc="Name of flowsheet result set to store data")
def __init__(self, dat=None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "SciPy-BFGS"
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p>Developer: Charles George Broyden, Roger Fletcher, Donald Goldfarb and David Shanno</p>"
"<p>Algorithm Type: Quasi Newton</p>"
"<p>Optimization Problems handled: Unconstrained Nonlinear Optimization, with variables > 1000 (L-BFGS)</p>"
"</html>")
self.options.add(name='upper',
default=10.0,
dtype=float,
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(name="ftol",
default=1.0e-9,
desc="Function abs tolerance termiantion condition",
dtype=float)
self.options.add(name="eps",
default=1.0e-11,
desc="Jacobian approximation step size",
dtype=float)
self.options.add(
name="maxeval",
default=1000000,
desc="maximum number of objective function evaluations",
dtype=int)
self.options.add(name="maxtime",
default=48.0,
desc="maximum time to allow for optimization (hours)",
dtype=float)
self.options.add(name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(name='Set Name',
default="SciPy-BFGS",
dtype=str,
desc="Name of flowsheet result set to store data")
def __init__(self, dat=None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "CMA-ES"
self.methodDescription = \
("This plugin just runs samples evaluetes all the flowsheet "
"samples and picks out the best one. If any samples have "
"not been evaluted (status -1), they will be evaluted. "
"This works with the currently selected data filter, and "
"is mostly used for testing.")
self.mp = True
self.mobj = False
self.requireScaling = False
self.minVars = 0
self.maxVars = 500
self.options.add(
name="Backup interval",
default=0,
desc=("Time between saving FOQUS session backups (sec)"
" ( < 15 no backup)"))
def __init__(self, dat=None):
'''
Initialize NLOPT optimization module
Args:
dat = foqus session object
'''
optimization.__init__(self, dat) # base class __init__
self.name = "NLopt" # Plugin name is actually comming from file
# name at this point so give file same name
# (with a *.py).
# Next is the description of the optimization
# Unfortunatly not all the HTML below works
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p>Steven G. Johnson, The NLopt nonlinear-optimization"
" package, http://ab-initio.mit.edu/nlopt <\p>"
"<p>Algorithm Type: Both derivative free & gradient based</p>"
"<p>Optimization Problems handling Capability: Nonlinear Optimization Problems with Nonlinear inequality constraints.</p>"
"<p>AUGLAG, COBYLA, and ISRES algorithms support nonlinear equality constraints as well.</p>"
"<p>NLopt contains several solvers by various authors"
"see the NLopt documentation for more information</p>"
"</html>")
self.available = nlopt_available # If plugin is available
self.description = "NLopt" #Short description
self.mp = False #Can evaluate objectives in parallel?
self.mobj = False #Can do multi-objective optimzation?
self.minVars = 2 #Minimum number of decision variables
self.maxVars = 10000 #Maximum number of decision variables
#
# Next up is the solver options appearing on the solver options
# page. If the dtype is not specified the option will guess it
# from the default value. Providing a list of valid values
# creates a dropdown box.
#
self.options.add( #NLopt contains a bunch of slovers this is the
name='Solver', #list of solvers DFO to select from
default="BOBYQA",
dtype=str,
validValues=[
"BOBYQA",
"COBYLA",
"Nelder-Mead",
"NEWUOA",
"PRAXIS",
"Sbplx",
"CRS2",
"DIRECT",
"DIRECT-L",
"DIRECT-L-RAND",
"ESCH",
"ISRES"],
desc="Solver")
self.options.add(
name='pop',
default=0,
dtype=int,
desc=("Initial Population size for CRS2 or ISRES other "
"methods do not use this paramter (<= 0 for default)"))
self.options.add(
name='init step',
default=0,
dtype=float,
desc="Initial step size for some local DFOs (<=0 default)")
self.options.add(
name='upper',
default=10.0,
dtype=float,
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(
name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(
name="tolfunabs",
default=1.0e-9,
desc="Function abs tolerance termiantion condition",
dtype=float)
self.options.add(
name="tolfunrel",
default=1.0e-9,
desc="Function relative tolerance termiantion condition",
dtype=float)
self.options.add(
name="tolxabs",
default=1.0e-9,
desc="X abs tolerance termiantion condition",
dtype=float)
self.options.add(
name="tolxrel",
default=1.0e-9,
desc="X relative tolerance termiantion condition",
dtype=float)
self.options.add(
name="maxeval",
default=0,
desc="maximum number of objective function evaluations",
dtype=int)
self.options.add(
name="maxtime",
default=48.0,
desc="maximum time to allow for optimization (hours)",
dtype=float)
self.options.add(
name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(
name='Set Name',
default="NLopt",
dtype=str,
desc="Name of flowsheet result set to store data")
self.options.add(
name='Backup Interval',
default=1.0,
dtype=float,
desc="How often (in hours) to save a backup session while "
" optimization is running. Less than 0.03 disables")
def __init__(self, dat=None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "CMA-ES"
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p><b>Covariacne Matrix Adaptation Evolutionary Strategy"
" (CMA-ES)</b></p>"
" Hansen, N. (2006). The CMA Evolution Strategy: A "
"Comparing Review. In J.A. Lozano, P. Larranga, I. Inza "
"and E. Bengoetxea (eds.). Towards a new evolutionary "
"computation. Advances in estimation of distribution "
"algorithms. pp. 75-102, Springer.<\p>"
"<p>This plugin makes use of the CMA-ES Python module "
"https://www.lri.fr/~hansen/cmaes_inmatlab.html#python. "
"This plugin provides a wrapper for the CMA-ES code "
"allowing it to work with FOQUS.</p></html>")
self.available = cma_available
self.description = "CMA-ES from ..."
self.mp = True
self.mobj = False
self.minVars = 2
self.maxVars = 10000
self.options.add(
name='upper',
default=10.0,
dtype=float, #don't need this if default is proper type
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(
name="tolfun",
default=None,
desc="Function range tolerance termiantion condition, "\
"The range of function values of the best objective "\
"over the last 10 + 30*N/popsize and all the "\
"evaluations in the most recent iteration, "\
"N=number of decision variables "\
"(null solver default 10**-11)",
dtype=float)
self.options.add(
name="tolstagnation",
default=None,
desc="stop after this number of iterations without "\
"improvment (null solver default, "\
"100+100*N**1.5/popsize, N = number of variables)",
dtype=float)
self.options.add(
name="tolx",
default=None,
desc="Tolerance for changes in decision variables, "\
"termiantion condition, stop if standard deviation of "\
"the distribution in all directions is less that tolx "\
"(null solver default 10**-11)",
dtype=float)
self.options.add(name="seed",
default=0,
desc="Random number seed (0 uses clock)")
self.options.add(
name="itmax",
default=0,
desc="Maximum number of iterations (0 go until converges)",
dtype=int)
self.options.add(
name="popsize",
default=6,
desc="Number of samples per iteration. The suggested"\
"number lacking another information and on a single "\
"processor is 4 + floor(3*loge(N)) where N is the "\
"number of decision variables" )
self.options.add(
name="sd0",
default=2,
desc="Initial standard deviation about starting point")
self.options.add(name="Results name",
default="CMA_Results",
desc="The name assigned to the flowsheet result set.")
self.options.add(
name="Restart out",
default="",
desc="Restart file for, {n} replaced by iteration index")
self.options.add(
name="Restart modulus",
default=1,
desc="Create restart file every x iterations (0 no restart)")
self.options.add(
name="Restart in",
default="",
desc="A file to restart from. (if empty string start new)")
self.options.add(name="It timeout",
default=3600,
desc="Iteration timeout (sec). unfinished sims fail")
self.options.add(
name="Max time",
default=0.0,
desc="Max time allowed for optimization (hours), "\
"not exact, but will not terminate sooner (0 for "\
"no time limit")
self.options.add(name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(
name="Backup interval",
default=10,
desc="Iterations between saving FOQUS session backup"\
" (0 no backup)")
self.options.add(name="Log Objective",
default="",
desc="Append objective mix/max after every iteration")
def __init__(self, dat=None):
'''
Initialize Snobfit optimization module
Args:
dat = foqus session object
'''
optimization.__init__(self, dat) # base class __init__
self.name = "Snobfit" # Plugin name is actually comming from file
# name at this point so give file same name
# (with a *.py).
# Next is the description of the optimization
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p>Developer: WALTRAUD HUYER and ARNOLD NEUMAIER</p>"
"<p>Algorithm Type: Branch & Local Fit</p>"
"<p>Optimization Problems handling Capability: Unconstrained Noisy Optimization problems with box bounds, and optional soft constraints</p>"
"</html>")
self.available = snobfit_available # If plugin is available
self.description = "Optimization Solver" #Short description
self.mp = False #Can evaluate objectives in parallel?
self.mobj = False #Can do multi-objective optimzation?
self.minVars = 2 #Minimum number of decision variables
self.maxVars = 10000 #Maximum number of decision variables
# Next up is the solver options appearing on the solver options
# page. If the dtype is not specified the option will guess it
# from the default value. Providing a list of valid values
# creates a dropdown box.
#******** FROZEN OPTIONS FOR NOW*****************
# self.options.add(
# name='bounds',
# dtype=object,
# desc="Box Bounds of decision variables")
# self.options.add(
# name='nreq',
# default=2,
# dtype=int,
# desc="Required Number of Suggested Evaluation Points Generated by SnobFit")
# self.options.add(
# name='p',
# default=0.5,
# dtype=float,
# desc="Probability of Generating a Class 4 Point")
#******************************************************
self.options.add(name='budget',
default=10000,
dtype=int,
desc="Limit on the number of function calls")
self.options.add(
name='minfcall',
default=500,
dtype=int,
desc="Minimum number of function values before considering stopping"
)
self.options.add(
name='nstop',
default=5,
dtype=int,
desc=
"Number of snobfit calls upto which no improvement is tolerated")
self.options.add(name="maxtime",
default=48.0,
desc="maximum time to allow for optimization (hours)",
dtype=float)
self.options.add(name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(name='Set Name',
default="SNOBFIT",
dtype=str,
desc="Name of flowsheet result set to store data")
self.options.add(
name='Backup Interval',
default=0.5,
dtype=float,
desc="How often (in hours) to save a backup session while "
" optimization is running. Less than 0.03 disables")
def __init__(self, dat=None):
'''
Initialize Snobfit optimization module
Args:
dat = foqus session object
'''
optimization.__init__(self, dat) # base class __init__
self.name = "Snobfit" # Plugin name is actually comming from file
# name at this point so give file same name
# (with a *.py).
# Next is the description of the optimization
self.methodDescription = \
("This solver provides robust and fast solution of noisy optimization problems with continuous variables varying within box bounds, possibly subject to additional soft constraints. Discrete variables are not supported")
self.available = snobfit_available # If plugin is available
self.description = "Optimization Solver" #Short description
self.mp = False #Can evaluate objectives in parallel?
self.mobj = False #Can do multi-objective optimzation?
self.minVars = 2 #Minimum number of decision variables
self.maxVars = 10000 #Maximum number of decision variables
# Next up is the solver options appearing on the solver options
# page. If the dtype is not specified the option will guess it
# from the default value. Providing a list of valid values
# creates a dropdown box.
#******** FROZEN OPTIONS FOR NOW*****************
# self.options.add(
# name='bounds',
# dtype=object,
# desc="Box Bounds of decision variables")
# self.options.add(
# name='nreq',
# default=2,
# dtype=int,
# desc="Required Number of Suggested Evaluation Points Generated by SnobFit")
# self.options.add(
# name='p',
# default=0.5,
# dtype=float,
# desc="Probability of Generating a Class 4 Point")
#******************************************************
self.options.add(
name='budget',
default=10000,
dtype=int,
desc="Limit on the number of function calls")
self.options.add(
name='minfcall',
default=500,
dtype=int,
desc="Minimum number of function values before considering stopping")
self.options.add(
name='nstop',
default=5,
dtype=int,
desc="Number of snobfit calls upto which no improvement is tolerated")
self.options.add(
name="maxtime",
default=48.0,
desc="maximum time to allow for optimization (hours)",
dtype=float)
self.options.add(
name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(
name='Set Name',
default="SNOBFIT",
dtype=str,
desc="Name of flowsheet result set to store data")
self.options.add(
name='Backup Interval',
default=0.5,
dtype=float,
desc="How often (in hours) to save a backup session while "
" optimization is running. Less than 0.03 disables")
def __init__(self, dat=None):
'''
Initialize CMA-ES optimization module
'''
optimization.__init__(self, dat)
self.name = "CMA-ES"
self.methodDescription = \
("<html>\n<head>"
".hangingindent {\n"
" margin-left: 22px ;\n"
" text-indent: -22px ;\n"
"}\n"
"</head>\n"
"<p class=\"hangingindent\">"
"<p><b>Covariance Matrix Adaptation Evolutionary Strategy"
" (CMA-ES)</b></p>"
"<p>Developer: Nikolaus Hansen</p>"
"<p>Algorithm Type: Genetic/Evolutionary</p>"
"<p>Optimization Problems handling Capability: Nonlinear, Non Convex, Unconstrained Optimization problems, with optional bounds on variables. Variable limit: 3-100</p>"
"<p>More Details found here: http://cma.gforge.inria.fr/"
"</html>")
self.available = cma_available
self.description = "CMA-ES from ..."
self.mp = True
self.mobj = False
self.minVars = 2
self.maxVars = 10000
self.options.add(
name='upper',
default=10.0,
dtype=float, #don't need this if default is proper type
desc="Upper bound on scaled variables (usually 10.0)")
self.options.add(name='lower',
default=0.0,
desc="Lower bound on scaled variables (usually 0.0)")
self.options.add(
name="tolfun",
default=None,
desc="Function range tolerance termiantion condition, "\
"The range of function values of the best objective "\
"over the last 10 + 30*N/popsize and all the "\
"evaluations in the most recent iteration, "\
"N=number of decision variables "\
"(null solver default 10**-11)",
dtype=float)
self.options.add(
name="tolstagnation",
default=None,
desc="stop after this number of iterations without "\
"improvment (null solver default, "\
"100+100*N**1.5/popsize, N = number of variables)",
dtype=float)
self.options.add(
name="tolx",
default=None,
desc="Tolerance for changes in decision variables, "\
"termiantion condition, stop if standard deviation of "\
"the distribution in all directions is less that tolx "\
"(null solver default 10**-11)",
dtype=float)
self.options.add(name="seed",
default=0,
desc="Random number seed (0 uses clock)")
self.options.add(
name="itmax",
default=0,
desc="Maximum number of iterations (0 go until converges)",
dtype=int)
self.options.add(
name="popsize",
default=6,
desc="Number of samples per iteration. The suggested"\
"number lacking another information and on a single "\
"processor is 4 + floor(3*loge(N)) where N is the "\
"number of decision variables" )
self.options.add(
name="sd0",
default=2,
desc="Initial standard deviation about starting point")
self.options.add(name="Results name",
default="CMA_Results",
desc="The name assigned to the flowsheet result set.")
self.options.add(
name="Restart out",
default="",
desc="Restart file for, {n} replaced by iteration index")
self.options.add(
name="Restart modulus",
default=1,
desc="Create restart file every x iterations (0 no restart)")
self.options.add(
name="Restart in",
default="",
desc="A file to restart from. (if empty string start new)")
self.options.add(name="It timeout",
default=3600,
desc="Iteration timeout (sec). unfinished sims fail")
self.options.add(
name="Max time",
default=0.0,
desc="Max time allowed for optimization (hours), "\
"not exact, but will not terminate sooner (0 for "\
"no time limit")
self.options.add(name="Save results",
default=True,
desc="Save all flowsheet results?")
self.options.add(
name="Backup interval",
default=10,
desc="Iterations between saving FOQUS session backup"\
" (0 no backup)")
self.options.add(name="Log Objective",
default="",
desc="Append objective mix/max after every iteration")
