/* 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 Kita problem.
* <p>
* Properties:
* <ul>
* <li>Disconnected Pareto set
* <li>Disconnected and concave Pareto front
* <li>Constrained
* <li>Maximization (objectives are negated)
* </ul>
* <p>
* References:
* <ol>
* <li>Kita, H., et al. (1996). "Multi-Objective Optimization by Means of
* the Thermodynamical Genetic Algorithm." Parallel Problem Solving from
* Nature — PPSN IV, Lecture Notes in Computer Science, Springer,
* pp. 504-512.
* <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 Kita extends AbstractProblem {
/**
* Constructs the Kita problem.
*/
public Kita() {
super(2, 2, 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) + y;
double f2 = 0.5*x + y + 1.0;
double c1 = 1.0/6.0*x + y - 13.0/2.0;
double c2 = 0.5*x + y - 15.0/2.0;
double c3 = 5.0*x + y - 30.0;
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);
}
@Override
public Solution newSolution() {
Solution solution = new Solution(2, 2, 3);
solution.setVariable(0, new RealVariable(0.0, 7.0));
solution.setVariable(1, new RealVariable(0.0, 7.0));
return solution;
}
}