runmultiverification.m

this file is a zip file for multi objective optimization by matlab.

% Script used for verification of the algorithm's capabilities
% This script uses the Riccati equation to validate the algorithm.
%
% The found minimum is compared to the actual minium for n=5 and
% x0=100 in the end

% By Kerry W. Lothrop <[e-mail]>
%
% Lothrop, K.W.: Conceptual Design Optimization of a Cis-Lunar
% Transportation Architecture Using Genetic Algorithms, Universit鋞
% Stuttgart, University of Colorado, 2003
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 1, or (at your option)
% any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details. A copy of the GNU 
% General Public License can be obtained from the 
% Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.


vf  = [ones(5,1).*4 -ones(5,1).*100 ones(5,1).*100];
vdi = [ones(5,1).*3 -ones(5,1).*100 ones(5,1).*100];
vii = [ones(3,1).*2 -ones(3,1).*2 ones(3,1).*2];
vb  = [ones(3,1) zeros(3,1) ones(3,1)];
designVectorProps = [vf; vdi; vii; vb];
initPop = multiinitializega(60, designVectorProps, 'multiVerificationObjFunc', [100]);
[bestSol, endPop, bestPop, traceInfo] = multiga(designVectorProps, ...
                                                'multiVerificationObjFunc', ...
                                                [100], ...
                                                initPop, ...
                                                [1E-6 1], ...
                                                ['maxGenTerm'], ...
                                                [300], ...
                                                ['normGeomSelect'], ...
                                                [0.08], ...
                                                [0.6 35], ...
                                                ['multiSimpleXover'], ...
                                                ['multiSimpleXover'], ...
                                                ['multiDepIntegerArithXover multiDepIntegerArithXover multiDepIntegerArithXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiDepIntegerHeuristicXover multiSimpleXover'], ...
                                                ['multiFloatArithXover multiFloatArithXover multiFloatArithXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiFloatHeuristicXover multiSimpleXover'], ...
                                                [0], ...
                                                [0], ...
                                                [0; 0; 0; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 0], ...
                                                [0; 0; 0; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 0], ...
                                                [0.05 4], ...
                                                ['multiBinaryMutation'], ...
                                                ['multiIndepIntegerMutation'], ...
                                                ['multiDepIntegerGaussMutation'], ...
                                                ['multiFloatGaussMutation'], ...
                                                [1], ...
                                                [1], ...
                                                [0.02 5], ...
                                                [0.02 5]);

disp('Design variable vector for the minimum value found:');
disp(bestSol(1:16));
disp('corresponding to the objective function value of:');
fprintf(1, '%.2f\n', -bestSol(17));

disp('The actual optimum:');
fprintf(1, '%.2f\n', riccati(10, 100));