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Generalized Simulated Annealing - GSA

version 1.0.0 (4.76 KB) by Jorge HV
GSA find the optimum of a function by the Generalized Simulated Annealing Method

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Updated 02 Dec 2019

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%GSA find the optimum of a function by the Generalized Simulated Annealing Method.
% Inputs:
f: objective function
x: initial solution vector
l: lower bounds
u: upper bounds
qv: visiting parameter
qa: acceptance parameter
Imax: maximum number of iterations
% Outputs:
xo: solution vector
fo: objective function value at solution vector (it must result in a scalar)
time: calculation time

Example (https://www.mathworks.com/help/gads/isolated-global-minimum.html):

x1 = [1;1]; x2 = [1e5;-1e5];
f = @(x)-10*sech(norm(x(:)-x1)) -20*sech((norm(x(:)-x2))*3e-4) -1;
[xo,fo,time] = GSA(f,[0;0],[-1e6;-1e6],[1e6;1e6],2.7,-5,800)

Global min: xo = 1.0e+05 *[1.0000 -1.0000]; fo = -21.

Cite As

Visbal, Jorge Homero Wilches, and Alessandro Martins Da Costa. “Algoritmo De Recocido Simulado Generalizado Para Matlab.” Ingenierı́a y Ciencia, vol. 15, no. 30, Universidad EAFIT, Nov. 2019, pp. 117–40, doi:10.17230/ingciencia.15.30.6.

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MATLAB Release Compatibility
Created with R2017b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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