x
= run(gs
,problem
)
[x
,fval
]
= run(gs
,problem
)
[x
,fval
,exitflag
]
= run(gs
,problem
)
[x
,fval
,exitflag
,output
]
= run(gs
,problem
)
[x
,fval
,exitflag
,output
,solutions
]
= run(gs
,problem
)
finds
a point x
= run(gs
,problem
)x
that solves the optimization
problem described in the problem
structure.
[
returns
the value of the objective function in x
,fval
]
= run(gs
,problem
)problem
at
the point x
.
[
returns
an exit flag describing the results of the multiple local searches.x
,fval
,exitflag
]
= run(gs
,problem
)
[
returns
an output structure describing the iterations of the run.x
,fval
,exitflag
,output
]
= run(gs
,problem
)
[
returns
a vector of solutions containing the distinct local minima found during
the run.x
,fval
,exitflag
,output
,solutions
]
= run(gs
,problem
)

A 

Problem structure. Create


Minimizing point of the objective function.  

Objective function value at the minimizer  

Describes the results of the multiple local searches. Values are:
 

A structure describing the iterations of the run. Fields in the structure:
 

A vector of

Use a default GlobalSearch
object to solve
the sixhump camel back problem (see Run the Solver):
gs = GlobalSearch; sixmin = @(x)(4*x(1)^2  2.1*x(1)^4 + x(1)^6/3 ... + x(1)*x(2)  4*x(2)^2 + 4*x(2)^4); problem = createOptimProblem('fmincon','x0',[1,2],... 'objective',sixmin,'lb',[3,3],'ub',[3,3]); [xmin,fmin,flag,outpt,allmins] = run(gs,problem);
A detailed description of the algorithm appears in GlobalSearch Algorithm. Ugray et al. [1] describes both the algorithm and the scattersearch method of generating trial points.
[1] Ugray, Zsolt, Leon Lasdon, John Plummer, Fred Glover, James Kelly, and Rafael Martí. Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization. INFORMS Journal on Computing, Vol. 19, No. 3, 2007, pp. 328–340.