00001 /* 00002 * This file is part of ACADO Toolkit. 00003 * 00004 * ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization. 00005 * Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau, 00006 * Milan Vukov, Rien Quirynen, KU Leuven. 00007 * Developed within the Optimization in Engineering Center (OPTEC) 00008 * under supervision of Moritz Diehl. All rights reserved. 00009 * 00010 * ACADO Toolkit is free software; you can redistribute it and/or 00011 * modify it under the terms of the GNU Lesser General Public 00012 * License as published by the Free Software Foundation; either 00013 * version 3 of the License, or (at your option) any later version. 00014 * 00015 * ACADO Toolkit is distributed in the hope that it will be useful, 00016 * but WITHOUT ANY WARRANTY; without even the implied warranty of 00017 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 00018 * Lesser General Public License for more details. 00019 * 00020 * You should have received a copy of the GNU Lesser General Public 00021 * License along with ACADO Toolkit; if not, write to the Free Software 00022 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 00023 * 00024 */ 00025 00026 00034 #include <acado_optimal_control.hpp> 00035 #include <acado_gnuplot.hpp> 00036 00037 00038 /* >>> start tutorial code >>> */ 00039 int main( ){ 00040 00041 USING_NAMESPACE_ACADO 00042 00043 00044 // INTRODUCE THE VARIABLES: 00045 // ------------------------- 00046 Parameter y1,y2,y3; 00047 00048 00049 // DEFINE AN OPTIMIZATION PROBLEM: 00050 // ------------------------------- 00051 NLP nlp; 00052 nlp.minimize( 0, y1 ); 00053 nlp.minimize( 1, y2 ); 00054 nlp.minimize( 2, y3 ); 00055 00056 nlp.subjectTo( -5.0 <= y1 <= 5.0 ); 00057 nlp.subjectTo( -5.0 <= y2 <= 5.0 ); 00058 nlp.subjectTo( -5.0 <= y3 <= 5.0 ); 00059 00060 nlp.subjectTo( y1*y1+y2*y2+y3*y3 <= 4.0 ); 00061 00062 00063 // DEFINE A MULTI-OBJECTIVE ALGORITHM AND SOLVE THE NLP: 00064 // ----------------------------------------------------- 00065 MultiObjectiveAlgorithm algorithm(nlp); 00066 00067 algorithm.set( PARETO_FRONT_GENERATION, PFG_NORMAL_BOUNDARY_INTERSECTION ); 00068 algorithm.set( PARETO_FRONT_DISCRETIZATION, 11 ); 00069 00070 // Minimize individual objective function 00071 algorithm.solveSingleObjective(0); 00072 00073 // Minimize individual objective function 00074 algorithm.solveSingleObjective(1); 00075 00076 // Minimize individual objective function 00077 algorithm.solveSingleObjective(2); 00078 00079 // Generate Pareto set 00080 algorithm.solve(); 00081 00082 algorithm.getWeights("scalar3_nbi_weights.txt"); 00083 00084 00085 // GET THE RESULT FOR THE PARETO FRONT AND PLOT IT: 00086 // ------------------------------------------------ 00087 VariablesGrid paretoFront; 00088 // algorithm.getParetoFrontWithFilter( paretoFront ); 00089 algorithm.getParetoFront( paretoFront ); 00090 00091 //GnuplotWindow window; 00092 //window.addSubplot3D( paretoFront, "Pareto Front y1 vs y2 vs y3","y1","y2", PM_POINTS ); 00093 //window.plot( ); 00094 00095 paretoFront.print(); 00096 00097 00098 // PRINT INFORMATION ABOUT THE ALGORITHM: 00099 // -------------------------------------- 00100 algorithm.printInfo(); 00101 00102 return 0; 00103 } 00104 /* <<< end tutorial code <<< */