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Optimize Function Using particleswarm, Problem-Based

This example shows how to minimize a function using particle swarm in the problem-based approach when the objective is a function file, possibly of unknown content (a "black box" function). The function to minimize, dejong5fcn(x), is included when you run this example.


Create a 2-D optimization variable x. The dejong5fcn function expects the variable to be a row vector, so specify x as a 2-element row vector.

x = optimvar("x",1,2);

To use dejong5fcn as the objective function, convert the function to an optimization expression using fcn2optimexpr.

fun = fcn2optimexpr(@dejong5fcn,x);

Create an optimization problem with the objective function fun.

prob = optimproblem("Objective",fun);

Set variable bounds from –50 to 50 in all components. When you specify scalar bounds, the software expands the bounds to all variables.

x.LowerBound = -50;
x.UpperBound = 50;

Solve the problem, specifying the particleswarm solver.

rng default % For reproducibility
[sol,fval] = solve(prob,"Solver","particleswarm")
Solving problem using particleswarm.
Optimization ended: relative change in the objective value 
over the last OPTIONS.MaxStallIterations iterations is less than OPTIONS.FunctionTolerance.
sol = struct with fields:
    x: [-31.9751 -31.9719]

fval = 0.9980

See Also

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