Documentation 
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)
x = run(gs,problem) finds a point x that solves the optimization problem described in the problem structure.
[x,fval] = run(gs,problem) returns the value of the objective function in problem at the point x.
[x,fval,exitflag] = run(gs,problem) returns an exit flag describing the results of the multiple local searches.
[x,fval,exitflag,output] = run(gs,problem) returns an output structure describing the iterations of the run.
[x,fval,exitflag,output,solutions] = run(gs,problem) returns a vector of solutions containing the distinct local minima found during the run.
gs 
A GlobalSearch object. 
problem 
Problem structure. Create problem with createOptimProblem or by exporting a problem structure from the Optimization app. problem must contain at least the following fields:

x 
Minimizing point of the objective function.  
fval 
Objective function value at the minimizer x.  
exitflag 
Describes the results of the multiple local searches. Values are:
 
output 
A structure describing the iterations of the run. Fields in the structure:
 
solutions 
A vector of GlobalOptimSolution objects containing the distinct local solutions found during the run. The vector is sorted by objective function value; the first element is best (smallest value). The object contains:

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.