Documentation

paretosearch

Find points in Pareto set

Description

example

x = paretosearch(fun,nvars) finds nondominated points of the multiobjective function fun. The nvars argument is the dimension of the optimization problem (number of decision variables).

example

x = paretosearch(fun,nvars,A,b) finds nondominated points subject to the linear inequalities A*x ≤ b. See Linear Inequality Constraints (Optimization Toolbox).

x = paretosearch(fun,nvars,A,b,Aeq,beq) finds nondominated points subject to the linear constraints Aeq*x = beq and A*x ≤ b. If no linear inequalities exist, set A = [] and b = [].

example

x = paretosearch(fun,nvars,A,b,Aeq,beq,lb,ub) defines a set of lower and upper bounds on the design variables in x, so that x is always in the range lb  x  ub. If no linear equalities exist, set Aeq = [] and beq = []. If x(i) has no lower bound, set lb(i) = -Inf. If x(i) has no upper bound, set ub(i) = Inf.

example

x = paretosearch(fun,nvars,A,b,Aeq,beq,lb,ub,nonlcon) applies the nonlinear inequalities c(x) defined in nonlcon. The paretosearch function finds nondominated points such that c(x) ≤ 0. If no bounds exist, set lb = [], ub = [], or both.

Note

Currently, paretosearch does not support nonlinear equality constraints ceq(x) = 0.

example

x = paretosearch(fun,nvars,A,b,Aeq,beq,lb,ub,nonlcon,options) finds nondominated points with the optimization options specified in options. Use optimoptions to set these options. If there are no nonlinear inequality or equality constraints, set nonlcon = [].

x = paretosearch(problem) finds the nondominated points for problem, where problem is a structure described in problem.

example

[x,fval] = paretosearch(___), for any input variables, returns the matrix fval, the value of all the fitness functions in fun for all the solutions (rows) in x. The output fval has nf columns, where nf is the number of objectives, and has the same number of rows as x.

example

[x,fval,exitflag,output] = paretosearch(___) also returns exitflag, an integer identifying the reason the algorithm stopped, and output, a structure that contains information about the solution process.

example

[x,fval,exitflag,output,residuals] = paretosearch(___) also returns residuals, a structure containing the constraint values at the solution points x.

Examples

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Find points on the Pareto front of a two-objective function of a two-dimensional variable.

fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
rng default % For reproducibility
x = paretosearch(fun,2);
Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

Plot the solution as a scatter plot.

plot(x(:,1),x(:,2),'m*')
xlabel('x(1)')
ylabel('x(2)') Theoretically, the solution of this problem is a straight line from [-2,-1] to [1,2]. paretosearch returns evenly-spaced points close to this line.

Create a Pareto front for a two-objective problem in two dimensions subject to the linear constraint x(1) + x(2) <= 1.

fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
A = [1,1];
b = 1;
rng default % For reproducibility
x = paretosearch(fun,2,A,b);
Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

Plot the solution as a scatter plot.

plot(x(:,1),x(:,2),'m*')
xlabel('x(1)')
ylabel('x(2)') Theoretically, the solution of this problem is a straight line from [-2,-1] to [0,1]. paretosearch returns evenly-spaced points close to this line.

Create a Pareto front for a two-objective problem in two dimensions subject to the bounds x(1) >= 0 and x(2) <= 1.

fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
lb = [0,-Inf]; % x(1) >= 0
ub = [Inf,1]; % x(2) <= 1
rng default % For reproducibility
x = paretosearch(fun,2,[],[],[],[],lb,ub);
Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

Plot the solution as a scatter plot.

plot(x(:,1),x(:,2),'m*')
xlabel('x(1)')
ylabel('x(2)') All of the solution points are on the constraint boundaries x(1) = 0 or x(2) = 1.

Create a Pareto front for a two-objective problem in two dimensions subject to bounds -1.1 <= x(i) <= 1.1 and the nonlinear constraint norm(x)^2 <= 1.2. The nonlinear constraint function appears at the end of this example, and works if you run this example as a live script. To run this example otherwise, include the nonlinear constraint function as a file on your MATLAB® path.

To better see the effect of the nonlinear constraint, set options to use a large Pareto set size.

rng default % For reproducibility
fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
lb = [-1.1,-1.1];
ub = [1.1,1.1];
options = optimoptions('paretosearch','ParetoSetSize',200);
x = paretosearch(fun,2,[],[],[],[],lb,ub,@circlecons,options);
Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

Plot the solution as a scatter plot. Include a plot of the circular constraint boundary.

figure
plot(x(:,1),x(:,2),'k*')
xlabel('x(1)')
ylabel('x(2)')
hold on
rectangle('Position',[-1.2 -1.2 2.4 2.4],'Curvature',1,'EdgeColor','r')
xlim([-1.2,0.5])
ylim([-0.5,1.2])
axis square
hold off The solution points that have positive x(1) values or negative x(2) values are close to the nonlinear constraint boundary.

function [c,ceq] = circlecons(x)
ceq = [];
c = norm(x)^2 - 1.2;
end

To monitor the progress of paretosearch, specify the 'psplotparetof' plot function.

fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
options = optimoptions('paretosearch','PlotFcn','psplotparetof');
lb = [-4,-4];
ub = -lb;
x = paretosearch(fun,2,[],[],[],[],lb,ub,[],options); Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

The solution looks like a quarter-circular arc with radius 18, which can be shown to be the analytical solution.

Obtain the Pareto front in both function space and parameter space by calling paretosearch with both the x and fval outputs. Set options to plot the Pareto set in both function space and parameter space.

fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
lb = [-4,-4];
ub = -lb;
options = optimoptions('paretosearch','PlotFcn',{'psplotparetof' 'psplotparetox'});
rng default % For reproducibility
[x,fval] = paretosearch(fun,2,[],[],[],[],lb,ub,[],options); Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

The analytical solution in objective function space is a quarter-circular arc of radius 18. In parameter space, the analytical solution is a straight line from [-2,-1] to [1,2]. The solution points are close to the analytical curves.

Set options to monitor the Pareto set solution process. Also, obtain more outputs from paretosearch to enable you to understand the solution process.

options = optimoptions('paretosearch','Display','iter',...
'PlotFcn',{'psplotparetof' 'psplotparetox'});
fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
lb = [-4,-4];
ub = -lb;
rng default % For reproducibility
[x,fval,exitflag,output] = paretosearch(fun,2,[],[],[],[],lb,ub,[],options); Iter   F-count   NumSolutions  Spread       Volume
0        60        11          -         3.7872e+02
1       386        12          -         3.4654e+02
2       702        27       9.4324e-01   2.9452e+02
3      1029        27          -         2.9904e+02
4      1357        40       0.0000e+00   3.0154e+02
5      1697        60       1.4903e-01   3.0369e+02
6      1841        60       1.4515e-01   3.0439e+02
7      1961        60       1.7716e-01   3.0465e+02
8      2075        60       1.6123e-01   3.0475e+02
9      2189        60       1.7419e-01   3.0449e+02

Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

Examine the additional outputs.

fprintf('Exit flag %d.\n',exitflag)
Exit flag 1.
disp(output)
iterations: 10
funccount: 2189
volume: 304.4256
averagedistance: 0.0215
maxconstraint: 0
message: 'Pareto set found that satisfies the constraints. ...'
rngstate: [1x1 struct]

Obtain and examine the Pareto front constraint residuals. Create a problem with the linear inequality constraint sum(x) <= -1/2 and the nonlinear inequality constraint norm(x)^2 <= 1.2. For improved accuracy, use 200 points on the Pareto front, and a ParetoSetChangeTolerance of 1e-7, and give the natural bounds -1.2 <= x(i) <= 1.2.

The nonlinear constraint function appears at the end of this example, and works if you run this example as a live script. To run this example otherwise, include the nonlinear constraint function as a file on your MATLAB® path.

fun = @(x)[norm(x-[1,2])^2;norm(x+[2,1])^2];
A = [1,1];
b = -1/2;
lb = [-1.2,-1.2];
ub = -lb;
nonlcon = @circlecons;
rng default % For reproducibility
options = optimoptions('paretosearch','ParetoSetChangeTolerance',1e-7,...
'PlotFcn',{'psplotparetof' 'psplotparetox'},'ParetoSetSize',200);

Call paretosearch using all outputs.

[x,fval,exitflag,output,residuals] = paretosearch(fun,2,A,b,[],[],lb,ub,nonlcon,options); Pareto set found that satisfies the constraints.

Optimization completed because the relative change in the volume of the Pareto set
is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within
'options.ConstraintTolerance'.

The inequality constraints reduce the size of the Pareto set compared to an unconstrained set. Examine the returned residuals.

fprintf('The maximum linear inequality constraint residual is %f.\n',max(residuals.ineqlin))
The maximum linear inequality constraint residual is 0.000000.
fprintf('The maximum nonlinear inequality constraint residual is %f.\n',max(residuals.ineqnonlin))
The maximum nonlinear inequality constraint residual is -0.000244.

The maximum returned residuals are negative, meaning that all the returned points are feasible. The maximum returned residuals are close to zero, meaning that each constraint is active for some points.

function [c,ceq] = circlecons(x)
ceq = [];
c = norm(x)^2 - 1.2;
end

Input Arguments

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Fitness functions to optimize, specified as a function handle or function name.

fun is a function that accepts a real row vector of doubles x of length nvars and returns a real vector F(x) of objective function values. For details on writing fun, see Compute Objective Functions.

If you set the UseVectorized option to true, then fun accepts a matrix of size n-by-nvars, where the matrix represents n individuals. fun returns a matrix of size n-by-m, where m is the number of objective functions. See Vectorize the Fitness Function.

Example: @(x)[sin(x),cos(x)]

Data Types: char | function_handle | string

Number of variables, specified as a positive integer. The solver passes row vectors of length nvars to fun.

Example: 4

Data Types: double

Linear inequality constraints, specified as a real matrix. A is an M-by-nvars matrix, where M is the number of inequalities.

A encodes the M linear inequalities

A*x <= b,

where x is the column vector of nvars variables x(:), and b is a column vector with M elements.

For example, to specify

x1 + 2x2 ≤ 10
3x1 + 4x2 ≤ 20
5x1 + 6x2 ≤ 30,

give these constraints:

A = [1,2;3,4;5,6];
b = [10;20;30];

Example: To specify that the control variables sum to 1 or less, give the constraints A = ones(1,N) and b = 1.

Data Types: double

Linear inequality constraints, specified as a real vector. b is an M-element vector related to the A matrix. If you pass b as a row vector, solvers internally convert b to the column vector b(:).

b encodes the M linear inequalities

A*x <= b,

where x is the column vector of N variables x(:), and A is a matrix of size M-by-N.

For example, to specify

x1 + 2x2 ≤ 10
3x1 + 4x2 ≤ 20
5x1 + 6x2 ≤ 30,

give these constraints:

A = [1,2;3,4;5,6];
b = [10;20;30];

Example: To specify that the control variables sum to 1 or less, give the constraints A = ones(1,N) and b = 1.

Data Types: double

Linear equality constraints, specified as a real matrix. Aeq is an Me-by-nvars matrix, where Me is the number of equalities.

Aeq encodes the Me linear equalities

Aeq*x = beq,

where x is the column vector of N variables x(:), and beq is a column vector with Me elements.

For example, to specify

x1 + 2x2 + 3x3 = 10
2x1 + 4x2 + x3 = 20,

give these constraints:

Aeq = [1,2,3;2,4,1];
beq = [10;20];

Example: To specify that the control variables sum to 1, give the constraints Aeq = ones(1,N) and beq = 1.

Data Types: double

Linear equality constraints, specified as a real vector. beq is an Me-element vector related to the Aeq matrix. If you pass beq as a row vector, solvers internally convert beq to the column vector beq(:).

beq encodes the Me linear equalities

Aeq*x = beq,

where x is the column vector of N variables x(:), and Aeq is a matrix of size Meq-by-N.

For example, to specify

x1 + 2x2 + 3x3 = 10
2x1 + 4x2 + x3 = 20,

give these constraints:

Aeq = [1,2,3;2,4,1];
beq = [10;20];

Example: To specify that the control variables sum to 1, give the constraints Aeq = ones(1,N) and beq = 1.

Data Types: double

Lower bounds, specified as a real vector or array of doubles. lb represents the lower bounds element-wise in lb  x  ub.

Internally, paretosearch converts an array lb to the vector lb(:).

Example: lb = [0;-Inf;4] means x(1) ≥ 0, x(3) ≥ 4.

Data Types: double

Upper bounds, specified as a real vector or array of doubles. ub represents the upper bounds element-wise in lb  x  ub.

Internally, paretosearch converts an array ub to the vector ub(:).

Example: ub = [Inf;4;10] means x(2) ≤ 4, x(3) ≤ 10.

Data Types: double

Nonlinear constraints, specified as a function handle or function name. nonlcon is a function that accepts a row vector x and returns two row vectors, c(x) and ceq(x).

• c(x) is the row vector of nonlinear inequality constraints at x. The paretosearch function attempts to satisfy c(x) <= 0 for all entries of c.

• ceq(x) must return [], because currently paretosearch does not support nonlinear equality constraints.

If you set the UseVectorized option to true, then nonlcon accepts a matrix of size n-by-nvars, where the matrix represents n individuals. nonlcon returns a matrix of size n-by-mc in the first argument, where mc is the number of nonlinear inequality constraints. See Vectorize the Fitness Function.

For example, x = paretosearch(@myfun,nvars,A,b,Aeq,beq,lb,ub,@mycon), where mycon is a MATLAB® function such as the following:

function [c,ceq] = mycon(x)
c = ...     % Compute nonlinear inequalities at x.
ceq = []    % No nonlinear equalities at x.

For more information, see Nonlinear Constraints (Optimization Toolbox).

Data Types: char | function_handle | string

Optimization options, specified as the output of optimoptions or as a structure.

optimoptions hides the options listed in italics; see Options that optimoptions Hides.

{} denotes the default value. See option details in Pattern Search Options.

Options for patternsearch and paretosearch

OptionDescriptionValues

ConstraintTolerance

Tolerance on constraints.

For an options structure, use TolCon.

Positive scalar | {1e-6}

Display

Level of display.

'off' | 'iter' | 'diagnose' | {'final'}

MaxFunctionEvaluations

Maximum number of objective function evaluations.

For an options structure, use MaxFunEvals.

Positive integer | {'2000*numberOfVariables'} for patternsearch, {'3000*(numberOfVariables+numberOfObjectives)'} for paretosearch, where numberOfVariables is the number of problem variables, and numberOfObjectives is the number of objective functions

MaxIterations

Maximum number of iterations.

For an options structure, use MaxIter.

Positive integer | {'100*numberOfVariables'} for patternsearch, {'100*(numberOfVariables+numberOfObjectives)'} for paretosearch, where numberOfVariables is the number of problem variables, and numberOfObjectives is the number of objective functions

MaxTime

Total time (in seconds) allowed for optimization.

For an options structure, use TimeLimit.

Positive scalar | {Inf}

MeshTolerance

Tolerance on the mesh size.

For an options structure, use TolMesh.

Positive scalar | {1e-6}

OutputFcn

Function that an optimization function calls at each iteration. Specify as a function handle or a cell array of function handles.

For an options structure, use OutputFcns.

PlotFcn

Plots of output from the pattern search. Specify as the name of a built-in plot function, a function handle, or a cell array of names of built-in plot functions or function handles.

For an options structure, use PlotFcns.

{[]} | For both patternsearch and paretosearch: 'psplotfuncount' | custom plot function

For paretosearch only with multiple objectives: 'psplotdistance' | 'psplotmaxconstr' | 'psplotparetof' | 'psplotparetox' | 'psplotspread' | 'psplotvolume'

For patternsearch only or paretosearch with a single objective: 'psplotbestf' | 'psplotmeshsize' | 'psplotbestx'

PollMethod

Polling strategy used in the pattern search.

{'GPSPositiveBasis2N'} | 'GPSPositiveBasisNp1' | 'GSSPositiveBasis2N' | 'GSSPositiveBasisNp1' | 'MADSPositiveBasis2N' | 'MADSPositiveBasisNp1'

For paretosearch only: {'GSSPositiveBasis2np2'}

UseParallel

Compute objective and nonlinear constraint functions in parallel. See Vectorized and Parallel Options (User Function Evaluation) and How to Use Parallel Processing in Global Optimization Toolbox.

true | {false}

UseVectorized

Specifies whether functions are vectorized. See Vectorized and Parallel Options (User Function Evaluation) and Vectorize the Objective and Constraint Functions.

For an options structure, use Vectorized = 'on' or 'off'.

true | {false}

Options for paretosearch Only

OptionDescriptionValues

InitialPoints

Initial points for paretosearch. Use one of these data types:

• Matrix with nvars columns, where each row represents one initial point.

• Structure containing the following fields (all fields are optional except X0):

• X0 — Matrix with nvars columns, where each row represents one initial point.

• Fvals — Matrix with numObjectives columns, where each row represents the objective function values at the corresponding point in X0.

• Cineq — Matrix with numIneq columns, where each row represents the nonlinear inequality constraint values at the corresponding point in X0.

paretosearch computes any missing values in the Fvals and Cineq fields.

Matrix with nvars columns | structure | {[]}

MinPollFraction

Minimum fraction of the pattern to poll.

Scalar from 0 through 1 | {0}

ParetoSetSize

Number of points in the Pareto set.

Positive integer | {'max(numberOfObjectives, 60)'}, where numberOfObjectives is the number of objective functions

ParetoSetChangeTolerance

The solver stops when the relative change in a stopping measure over a window of iterations is less than or equal to ParetoSetChangeTolerance.

• For three or fewer objectives, paretosearch uses the volume and spread measures.

• For four or more objectives, paretosearch uses the spread and distance measures.

The solver stops when the relative change in any applicable measure is less than ParetoSetChangeTolerance, or the maximum of the squared Fourier transforms of the time series of these measures is relatively small. See paretosearch Algorithm.

Note

Setting ParetoSetChangeTolerance < sqrt(eps) ~ 1.5e-8 is not recommended.

Positive scalar | {1e-4}

Options for patternsearch Only

OptionDescriptionValues
Cache

With Cache set to 'on', patternsearch keeps a history of the mesh points it polls. At subsequent iterations, patternsearch does not poll points close to those already polled. Use this option if patternsearch runs slowly while computing the objective function. If the objective function is stochastic, do not use this option.

'on' | {'off'}

CacheSize

Size of the history.

Positive scalar | {1e4}

CacheTol

Largest distance from the current mesh point to any point in the history in order for patternsearch to avoid polling the current point. Use if Cache option is set to 'on'.

Positive scalar | {eps}

FunctionTolerance

Tolerance on the function. Iterations stop if the change in function value is less than FunctionTolerance and the mesh size is less than StepTolerance. This option does not apply to MADS polling.

For an options structure, use TolFun.

Positive scalar | {1e-6}

InitialMeshSize

Initial mesh size for the algorithm. See How Pattern Search Polling Works.

Positive scalar | {1.0}

InitialPenalty

Initial value of the penalty parameter. See Nonlinear Constraint Solver Algorithm.

Positive scalar | {10}

MaxMeshSize

Maximum mesh size used in a poll or search step. See How Pattern Search Polling Works.

Positive scalar | {Inf}

MeshContractionFactor

Mesh contraction factor for unsuccessful iteration.

For an options structure, use MeshContraction.

Positive scalar | {0.5}

MeshExpansionFactor

Mesh expansion factor for successful iteration.

For an options structure, use MeshExpansion.

Positive scalar | {2.0}

MeshRotate

Rotate the pattern before declaring a point to be optimum. See Mesh Options.

'off' | {'on'}

PenaltyFactor

Penalty update parameter. See Nonlinear Constraint Solver Algorithm.

Positive scalar | {100}

PlotInterval

Specifies that plot functions are called at every interval.

positive integer | {1}

PollOrderAlgorithm

Order of poll directions in pattern search.

For an options structure, use PollingOrder.

'Random' | 'Success' | {'Consecutive'}

ScaleMesh

Automatic scaling of variables.

For an options structure, use ScaleMesh = 'on' or 'off'.

{true}| false

SearchFcn

Type of search used in pattern search. Specify as a name or a function handle.

For an options structure, use SearchMethod.

'GPSPositiveBasis2N' | 'GPSPositiveBasisNp1' | 'GSSPositiveBasis2N' | 'GSSPositiveBasisNp1' | 'MADSPositiveBasis2N' | 'MADSPositiveBasisNp1' | 'searchga' | 'searchlhs' | 'searchneldermead' | {[]} | custom search function

StepTolerance

Tolerance on the variable. Iterations stop if both the change in position and the mesh size are less than StepTolerance. This option does not apply to MADS polling.

For an options structure, use TolX.

Positive scalar | {1e-6}

TolBind

Binding tolerance. See Constraint Parameters.

Positive scalar | {1e-3}

UseCompletePoll

Complete poll around the current point. See How Pattern Search Polling Works.

For an options structure, use CompletePoll = 'on' or 'off'.

true | {false}

UseCompleteSearch

Complete search around current point when the search method is a poll method. See Searching and Polling.

For an options structure, use CompleteSearch = 'on' or 'off'.

true | {false}

Example: options = optimoptions('paretosearch','Display','none','UseParallel',true)

Problem structure, specified as a structure with the following fields:

• objective — Objective function

• x0 — Starting point

• Aineq — Matrix for linear inequality constraints

• bineq — Vector for linear inequality constraints

• Aeq — Matrix for linear equality constraints

• beq — Vector for linear equality constraints

• lb — Lower bound for x

• ub — Upper bound for x

• nonlcon — Nonlinear constraint function

• solver'paretosearch'

• options — Options created with optimoptions

• rngstate — Optional field to reset the state of the random number generator

Note

All fields in problem are required, except for rngstate, which is optional.

Data Types: struct

Output Arguments

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Pareto points, returned as an m-by-nvars array, where m is the number of points on the Pareto front. Each row of x represents one point on the Pareto front.

Function values on the Pareto front, returned as an m-by-nf array. m is the number of points on the Pareto front, and nf is the number of fitness functions. Each row of fval represents the function values at one Pareto point in x.

Reason paretosearch stopped, returned as one of the integer values in this table.

Exit FlagStopping Condition
1

One of the following conditions is met.

• Mesh size of all incumbents is less than options.MeshTolerance and constraints (if any) are satisfied to within options.ConstraintTolerance.

• Relative change in the spread of the Pareto set is less than options.ParetoSetChangeTolerance and constraints (if any) are satisfied to within options.ConstraintTolerance.

• Relative change in the volume of the Pareto set is less than options.ParetoSetChangeTolerance and constraints (if any) are satisfied to within options.ConstraintTolerance.

0Number of iterations exceeds options.MaxIterations, or the number of function evaluations exceeds options.MaxFunctionEvaluations.
-1

Optimization is stopped by an output function or plot function.

-2Solver cannot find a point satisfying all the constraints.
-5Optimization time exceeds options.MaxTime.

Information about the optimization process, returned as a structure with these fields:

• iterations — Total number of iterations.

• funccount — Total number of function evaluations.

• volume — Hyper-volume of the set formed from the Pareto points in function space. See Definitions for paretosearch Algorithm.

• averagedistance — Average distance measure of the Pareto points in function space. See Definitions for paretosearch Algorithm.

• spread — Average spread measure of the Pareto points. See Definitions for paretosearch Algorithm.

• maxconstraint — Maximum constraint violation, if any.

• message — Reason why the algorithm terminated.

• rngstate — State of the MATLAB random number generator just before the algorithm starts. You can use the values in rngstate to reproduce the output when you use a random poll method such as 'MADSPositiveBasis2N' or when you use the default quasirandom method of creating the initial population. See Reproduce Results, which discusses the identical technique for ga.

Constraint residuals at x, returned as a structure with these fields (a glossary of the field size terms and entries follows the table).

Field NameField SizeEntries
lowerm-by-nvarslbx
upperm-by-nvarsxub
ineqlinm-by-nconA*x - b
eqlinm-by-ncon|Aeq*x - b|
ineqnonlinm-by-nconc(x)
• m — Number of returned points x on the Pareto front

• nvars — Number of control variables

• ncon — Number of constraints of the relevant type (such as number of rows of A or number of returned nonlinear equalities)

• c(x) — Numeric values of the nonlinear constraint functions

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Nondominated

Nondominated points, also called noninferior points, are points for which no other point has lower values of all objective functions. In other words, for nondominated points, none of the objective function values can be improved (lowered) without raising other objective function values. See What Is Multiobjective Optimization?.

Algorithms

paretosearch uses a pattern search to search for points on the Pareto front. For details, see paretosearch Algorithm.