To control the surrogate optimization algorithm, use the following options.
InitialPoints
— Specify initial points in one of two
ways.
Matrix — Each row of the matrix represents an initial point. The
length of each row is the same as the number of elements in the
bounds lb
or ub
. The number of
rows is arbitrary. surrogateopt
uses all the rows
to construct the initial surrogate. If there are fewer than
MinSurrogatePoints
rows, then
surrogateopt
generates the remaining initial
points. surrogateopt
evaluates the objective
function at each initial point.
Structure — The structure contains the field X
and, optionally, the field Fval
. The
X
field contains a matrix where each row
represents an initial point. The Fval
field
contains a vector representing the objective function values at each
point in X
. Passing Fval
saves
time for the solver.
MinSurrogatePoints
— Number of initial points used for
constructing the surrogate. Larger values lead to a more accurate finished
surrogate, but take more time to finish the surrogate.
surrogateopt
creates this number of random points
after each switch to the random generation phase. See Surrogate Optimization Algorithm.
MinSampleDistance
— This option controls two aspects of
the algorithm.
During the phase to estimate the minimum value of the
surrogate, the algorithm generates random points at which to
evaluate the surrogate. If any of these points are closer than
MinSampleDistance
to any previous point
whose objective function value was evaluated, then
surrogateopt
discards the newly
generated points and does not evaluate them.
If surrogateopt
discards all of the
random points, then it does not try to minimize the surrogate
and, instead, switches to the random generation phase. If the
surrogateoptplot
plot function is
running, then it marks this switch with a blue vertical
line.
For details, see Surrogate Optimization Algorithm.
Generally, the algorithm stops only when it reaches a limit that you set. There
are three limits that you can set using optimoptions
.
Additionally, a plot function or output function can halt the solver.
Stopping Option  Stopping Test  Exit Flag 

MaxFunctionEvaluations  The solver stops after it completes
MaxFunctionEvaluations function evaluations.
When computing in parallel, the solver stops all workers after a
worker returns with the final function evaluation, leaving some
computations incomplete and unused.  0 
MaxTime  The solver stops after it reaches MaxTime
seconds from the start of the optimization, as measured by tic / toc . The solver
does not interrupt a function evaluation in progress, so the actual
compute time can exceed MaxTime .  0 
ObjectiveLimit  The solver stops if it obtains an objective function value less
than or equal to ObjectiveLimit .  1 
OutputFcn or
PlotFcn  An OutputFcn or PlotFcn can
halt the iterations.  1 
Bounds lb and ub  If an entry in lb exceeds the corresponding
entry in ub , the solver stops because the bounds
are inconsistent.  2 
Set the Display
option to control what
surrogateopt
returns to the command line.
'final'
— Return only the exit message. This is the
default behavior.
'iter'
— Return iterative display.
'off'
or the equivalent 'none'
— No
commandline display.
With an iterative display, the solver returns the following information in table format.
Fcount
— Number of function evaluations
Time(s)
— Time in seconds since the solver
started
Best Fval
— Lowest objective function value
obtained
Current Fval
— Latest objective function value
An output function can halt the solver or perform a computation at each iteration.
To include an output function, set the OutputFcn
option to
@myoutputfcn
, where myoutputfcn
is a
function with the syntax described in the next paragraph. This syntax is the same as
for Optimization
Toolbox™ output functions, but with different meanings of the
x
and optimValues
arguments. For
information about those output functions, see Output Function Syntax (Optimization Toolbox).
For an example of an output function, see Integer Optimization with Custom Output Function.
The syntax of an output function is:
stop = outfun(x,optimValues,state)
surrogateopt
passes the values of x
,
optimValues
, and state
to the output
function (outfun
, in this case) at each iteration. The output
function returns stop
, a Boolean value (true
or false
) indicating whether to stop
surrogateopt
.
x
— The input argument x
is the best
point found so far, meaning the point with the lowest objective function
value.
optimValues
— This input argument is a structure
containing the following fields. For more information about these fields,
see Surrogate Optimization Algorithm.
optimValues
Structure
Field Name  Contents 

currentFlag  How the current point was created.

currentFval  Objective function value at the current point 
currentX  Current point 
elapsedtime  Time in seconds since the solver started 
flag  How the best point was created

funccount  Total number of objective function evaluations 
fval  Lowest objective function value encountered 
incumbentFlag  How the incumbent point was created

incumbentFval  Objective function value at the incumbent point 
incumbentX  Incumbent point, meaning the best point found since the last phase shift to random sampling 
iteration  Same as 
surrogateReset  Boolean value indicating that the current iteration resets the model and switches to random sampling 
surrogateResetCount  Total number of times that

state
— This input argument is the state of the
algorithm, specified as one of these values.
'init'
— The algorithm is in the initial
state before the first iteration. When the algorithm is in this
state, you can set up plot axes or other data structures or open
files.
When state
is
'init'
, the input arguments
x
and
optimValues.fval
are empty
([]
) because
surrogateopt
is designed for
timeconsuming objective functions, and so does not evaluate
the objective function before calling the initialization
step.
'iter'
— The algorithm just evaluated the
objective function. You perform most calculations and view most
displays when the algorithm is in this state.
'done'
— The algorithm performed its final
objective function evaluation. When the algorithm is in this
state, you can close files, finish plots, or prepare in other
ways for surrogateopt
to stop.
A plot function displays information at each iteration. You can pause or halt the
solver by clicking buttons on the plot. To include a plot function, set the
PlotFcn
option to a function handle or cell array of function
handles to plot functions. The three builtin plot functions are:
@optimplotfval
(default) — Shows the best function
value. If you do not choose a plot function,
surrogateopt
uses
@optimplotfval
.
@optimplotx
— Shows the best point found as a bar
plot.
@surrogateoptplot
— Shows the current objective
function value, best function value, and information about the algorithm
phase. See Interpret surrogateoptplot.
You can write a custom plot function using the syntax of an Output Function. For an example,
examine the code for @surrogateoptplot
by entering type
surrogateoptplot
at the MATLAB^{®} command line.
If you set the 'UseParallel'
option to true
,
surrogateopt
computes in parallel. Computing in parallel
requires a Parallel
Computing Toolbox™ license. For details, see Surrogate Optimization Algorithm.
When you set the name of a checkpoint file using the
CheckpointFile
option, surrogateopt
writes data to the file after each iteration, which enables the function to resume
the optimization from the current state. When restarting,
surrogateopt
does not evaluate the objective function value
at previously evaluated points.
A checkpoint file can be a file path such as
"C:\Documents\MATLAB\check1.mat"
or a file name such as
'checkpoint1June2019.mat'
. If you specify a file name without
a path, surrogateopt
saves the checkpoint file in the current
folder.
You can change only the following options when resuming the optimization:
CheckpointFile
Display
MaxFunctionEvaluations
MaxTime
MinSurrogatePoints
ObjectiveLimit
OutputFcn
PlotFcn
UseParallel
To resume the optimization from a checkpoint file, call
surrogateopt
with the file name as the first
argument.
[x,fval,exitflag,output] = surrogateopt('check1.mat')
To resume the optimization using new options, include the new options as the second argument.
opts = optimoptions(options,'MaxFunctionEvaluations',500); [x,fval,exitflag,output] = surrogateopt('check1.mat',opts)
During the restart, surrogateopt
runs any output functions
and plot functions, based on the original function evaluations. So, for example, you
can create a different plot based on an optimization that already ran. See Work with Checkpoint Files.
surrogateopt
does not save all details of the state in
the checkpoint file. Therefore, subsequent iterations can differ from the
iterations that the solver takes without stopping at the checkpointed
state.
Checkpointing takes time. This overhead is especially noticeable for functions that otherwise take little time to evaluate.
Do not resume surrogateopt
from a checkpoint file created with a
different MATLAB version. surrogateopt
can error or give inconsistent
results.