/* 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.Solution;
import org.moeaframework.core.variable.RealVariable;
import org.moeaframework.problem.AbstractProblem;
/**
* The Viennet (4) problem.
* <p>
* Properties:
* <ul>
* <li>Connected and asymmetric Pareto set
* <li>Curved Pareto front
* <li>Constrained
* </ul>
* <p>
* References:
* <ol>
* <li>Viennet, R., et al (1996). "Multicriteria Optimization Using a
* Genetic Algorithm for Determining a Pareto Set." International
* Journal of Systems Science, 27(2):255-260.
* <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 Viennet4 extends AbstractProblem {
/**
* Constructs the Viennet (4) problem.
*/
public Viennet4() {
super(2, 3, 3);
}
@Override
public void evaluate(Solution solution) {
double x = ((RealVariable)solution.getVariable(0)).getValue();
double y = ((RealVariable)solution.getVariable(1)).getValue();
double f1 = Math.pow(x - 2.0, 2.0) / 2.0 +
Math.pow(y + 1.0, 2.0) / 13.0 + 3.0;
double f2 = Math.pow(x + y - 3.0, 2.0) / 175.0 +
Math.pow(2.0*y - x, 2.0) / 17.0 - 13.0;
double f3 = Math.pow(3.0*x - 2.0*y + 4.0, 2.0) / 8.0 +
Math.pow(x - y + 1.0, 2.0) / 27.0 + 15.0;
//subtract Double.MIN_VALUE so that the constraint is satisfied only if
//its values is strictly greater than 0
double c1 = -4.0*x + 4.0 - y - Double.MIN_VALUE;
double c2 = x + 1 - Double.MIN_VALUE;
double c3 = y - x + 2.0 - Double.MIN_VALUE;
solution.setObjective(0, f1);
solution.setObjective(1, f2);
solution.setObjective(2, f3);
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);
}
@Override
public Solution newSolution() {
Solution solution = new Solution(2, 3, 3);
solution.setVariable(0, new RealVariable(-4.0, 4.0));
solution.setVariable(1, new RealVariable(-4.0, 4.0));
return solution;
}
}