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This example shows how to generate and plot
a Pareto front for a 2-D multiobjective function using `fgoalattain`

.

The two objectives in this example are shifted and scaled versions of the convex function $$\sqrt{1+{x}^{2}}$$.

```
function f = simple_mult(x)
f(:,1) = sqrt(1+x.^2);
f(:,2) = 4 + 2*sqrt(1+(x-1).^2);
```

Both components are increasing as *x* decreases
below 0 or increases above 1. In between 0 and 1, *f*_{1}(*x*) is
increasing and *f*_{2}(*x*) is
decreasing, so there is a tradeoff region.

t = linspace(-0.5,1.5); F = simple_mult(t); plot(t,F,'LineWidth',2) hold on plot([0,0],[0,8],'g--'); plot([1,1],[0,8],'g--'); plot([0,1],[1,6],'k.','MarkerSize',15); text(-0.25,1.5,'Minimum(f_1(x))') text(.75,5.5,'Minimum(f_2(x))') hold off legend('f_1(x)','f_2(x)') xlabel({'x';'Tradeoff region between the green lines'})

To find the Pareto front, first find the unconstrained
minima of the two functions. In this case, you can see by inspection
that the minimum of *f*_{1}(*x*) is
1, and the minimum of *f*_{2}(*x*) is
6, but in general you might need to use an optimization routine.

In general, write a function that returns a particular component of the multiobjective function.

function z = pickindex(x,k) z = simple_mult(x); % evaluate both objectives z = z(k); % return objective k

Then find the minimum of each component using an optimization
solver. You can use `fminbnd`

in this case, or `fminunc`

for
higher-dimensional problems.

k = 1; [min1,minfn1] = fminbnd(@(x)pickindex(x,k),-1,2); k = 2; [min2,minfn2] = fminbnd(@(x)pickindex(x,k),-1,2);

Set goals that are the unconstrained optima for each component. You can simultaneously achieve these goals only if the multiobjective functions do not interfere with each other, meaning there is no tradeoff.

goal = [minfn1,minfn2];

To calculate the Pareto front, take weight vectors [*a*,1–*a*]
for *a* from 0 through 1. Solve the goal attainment
problem, setting the weights to the various values.

nf = 2; % number of objective functions N = 50; % number of points for plotting onen = 1/N; x = zeros(N+1,1); f = zeros(N+1,nf); fun = @simple_mult; x0 = 0.5; options = optimoptions('fgoalattain','Display','off'); for r = 0:N t = onen*r; % 0 through 1 weight = [t,1-t]; [x(r+1,:),f(r+1,:)] = fgoalattain(fun,x0,goal,weight,... [],[],[],[],[],[],[],options); end figure plot(f(:,1),f(:,2),'k.'); xlabel('f_1') ylabel('f_2')

You can see the tradeoff between the two objectives.