/* Copyright 2009-2015 David Hadka
*
* This file is part of the MOEA Framework.
*
* The MOEA Framework is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or (at your
* option) any later version.
*
* The MOEA Framework 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 Lesser General Public
* License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with the MOEA Framework. If not, see <http://www.gnu.org/licenses/>.
*/
package org.moeaframework.problem.misc;
import org.moeaframework.core.PRNG;
import org.moeaframework.core.Solution;
import org.moeaframework.core.variable.EncodingUtils;
import org.moeaframework.problem.AbstractProblem;
import org.moeaframework.problem.AnalyticalProblem;
/**
* The Jimenez problem. The Pareto set is defined by the line from
* {@code (40, 15)} to {@code (50, 0)}.
* <p>
* Properties:
* <ul>
* <li>Connected and symmetric Pareto set
* <li>Convex Pareto front
* <li>Constrained
* <li>Maximization (objectives are negated)
* </ul>
* <p>
* References:
* <ol>
* <li>Jimenez, F. and Verdegay, J. L. (1998). "Constrained Multiobjective
* Optimization by Evolutionary Algorithms." Proceedings of the
* International ICSC Symposium on Engineering of Intelligent Systems,
* pp. 266-271.
* <li>Van Veldhuizen, D. A (1999). "Multiobjective Evolutionary Algorithms:
* Classifications, Analyses, and New Innovations." Air Force Institute
* of Technology, Ph.D. Thesis, Appendix B.
* </ol>
*/
public class Jimenez extends AbstractProblem implements AnalyticalProblem {
/**
* Constructs the Jimenez problem.
*/
public Jimenez() {
super(2, 2, 4);
}
@Override
public void evaluate(Solution solution) {
double x = EncodingUtils.getReal(solution.getVariable(0));
double y = EncodingUtils.getReal(solution.getVariable(1));
double f1 = 5.0*x + 3.0*y;
double f2 = 2.0*x + 8.0*y;
double c1 = x + 4.0*y - 100.0;
double c2 = 3.0*x + 2.0*y - 150.0;
double c3 = 200.0 - 5.0*x - 3.0*y;
double c4 = 75.0 - 2.0*x - 8.0*y;
solution.setObjective(0, -f1);
solution.setObjective(1, -f2);
solution.setConstraint(0, c1 <= 0.0 ? 0.0 : c1);
solution.setConstraint(1, c2 <= 0.0 ? 0.0 : c2);
solution.setConstraint(2, c3 <= 0.0 ? 0.0 : c3);
solution.setConstraint(3, c4 <= 0.0 ? 0.0 : c4);
}
@Override
public Solution newSolution() {
Solution solution = new Solution(2, 2, 4);
solution.setVariable(0, EncodingUtils.newReal(0.0, 50.0));
solution.setVariable(1, EncodingUtils.newReal(0.0, 50.0));
return solution;
}
@Override
public Solution generate() {
Solution solution = newSolution();
double p = PRNG.nextDouble(0.0, 1.0);
EncodingUtils.setReal(solution.getVariable(0), 40.0 + 10.0*p);
EncodingUtils.setReal(solution.getVariable(1), 15.0 - 15.0*p);
evaluate(solution);
return solution;
}
}