Find constrained minima of a real multi-objective function.

Attention: Valid only with Altair Advanced Optimization Extension


x = moga(@func,x0)

x = moga(@func,x0,A,b)

x = moga(@func,x0,A,b,Aeq,beq)

x = moga(@func,x0,A,b,Aeq,beq,lb,ub)

x = moga(@func,x0,A,b,Aeq,beq,lb,ub,nonlcon)

x = moga(@func,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)

[x,fval,info,output] = moga(...)


The function to minimize.
An estimate of the location of a minimum.
A matrix used to compute A*x for inequality contraints.
Use [ ] if unneeded.
The upper bound of the inequality constraints A*x<=b.
Use [ ] if unneeded.
A matrix used to compute Aeq*x for equality contraints.
Use [ ] if unneeded.
The upper bound of the equality constraints Aeq*x=beq.
Use [ ] if unneeded.
The design variable lower bounds.
Use [ ] if unbounded. Support for this option is limited. See comments.
The design variable upper bounds.
Use [ ] if unbounded. Support for this option is limited. See comments.
The nonlinear constraints function.
The function signature is as follows:
function [c, ceq] = ConFunc(x)
, where
c and ceq contain inequality and equality contraints, respectively. The ceq output can be omitted if it is empty.
Inequality constraints are assumed to have upper bounds of 0, and equality constraints are equal to 0. For constraints of the form g(x) < b, where b ~= 0, the recommended best practice is to write the constraint as (g(x) - b) / abs(b) < 0.
Use [ ] if unused and the options argument is needed.
A struct containing options settings.
See mogaoptimset for details.


The locations of the multi-objective minima.
The multi-objective function minima.
The convergence status flag.
  • info = 0: reached maximum number of iterations.
  • info = 1: the function converged.
  • info = 3: a constraint violation within TolCon occurred.
A struct containing iteration details. The members are as follows.
  • Pareto: a logical matrix indicating which samples belong to the Pareto Front. Each column contains the front information for an iteration.
  • nfev: the number of function evaluations.
  • xiter: the candidate solution at each iteration.
  • fvaliter: the objective function values at each iteration.
  • coniter: the constraint values at each iteration. The columns will contain the constraint function values in the following order: linear inequality contraints, linear equality constraints, nonlinear inequality contraints, nonlinear equality constraints.


Plot the iterations and Pareto Front for the function ObjFunc.
function obj = ObjFunc(x)
    obj = zeros(2,1);
    obj(1) = 2*(x(1)-3)^2 + 4*(x(2)-2)^2 + 6;
    obj(2) = 2*(x(1)-3)^2 + 4*(x(2)+2)^2 + 6;

init = [2; 0];
lowerBound = [1, -5];
upperBound = [5, 5];

options = mogaoptimset('MaxIter', 40, 'Seed', 2017);
[x,fval,info,output] = moga(@ObjFunc,init,[],[],[],[],lowerBound,upperBound,[],options);

obj1 = output.fvaliter(:,1);
obj2 = output.fvaliter(:,2);
scatter(obj1, obj2);
hold on;

obj1P = fval(:,1);
obj2P = fval(:,2);
scatter(obj1P, obj2P);
legend('Iteration History','Pareto Front');

Figure 1. moga figure 1
Modify the previous example to pass extra parameters to the objectives function using a function handle.
function obj = ObjFunc(x,p1,p2)
    obj = zeros(2,1);
    obj(1) = 2*(x(1)-3)^2 + 4*(x(2)-2)^2 + p1;
    obj(2) = 2*(x(1)-3)^2 + 4*(x(2)+2)^2 + p2;

handle = @(x) ObjFunc(x,7,8);
[x,fval] = moga(handle,init,[],[],[],[],lowerBound,upperBound,[],options);


moga uses a Multi-Objective Genetic Algorithm.

See the fmincon optimization tutorial for an example with nonlinear constraints.

Options are specified with mogaoptimset. The defaults are as follows.
  • MaxIter: 50
  • MaxFail: 20000
  • PopulationSize: 0 (the algorithm chooses)
  • TolCon: 0.5 (%)
  • CrowdDist: 0
  • Seed: 0
  • Display: 'off'

Unbounded limits design variable limits are not fully supported and are set to -1000 and 1000. Use of large limits is discouraged due to the size of the search area.

To pass additional parameters to a function argument, use an anonymous function.