Simulated Annealing Optimization 1.0

Simulated annealing is an optimization algorithm that skips local minimun. It uses a variation of Metropolis algorithm to perform the search of the minimun. It is recomendable to use it before another minimun search algorithm to track the global minimun instead of a local ones.Usage: [x0,f0]sim_anl(f,x0,l,u,Mmax,TolFun)INPUTS: f = a function handle x0 = a ninitial guess for the minimun l = a lower bound for minimun u = a upper bound for minimun Mmax = maximun number of temperatures TolFun = tolerancia de la funcidoDlnOUTPUTS: x0 = candidate to global minimun founded f0 = value of function on x0Example:The six-hump camelback function:camel= @(x)(4-2.1*x(1).^2+x(1).^4/3).*x(1).^2+x(1).*x(2)+4*(x(2).^2-1).*x(2).^2;has a doble minimun at f(-0.0898,0.7126) = f(0.0898,-0.7126) = -1.0316this code works with it as follows:[x0,f0]=sim_anl(camel,[0,0],[-10,-10],[10,10],400)and we get:x0=[-0.0897 0.7126]

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Bound,  Candidate,  Founded,  Funci,  Maximun,  Outputs,  Temperatures,  Tolerancia,  Tolfun,  Upper
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