/* Copyright 2009-2016 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.EncodingUtils;
import org.moeaframework.problem.AbstractProblem;
import org.moeaframework.problem.AnalyticalProblem;
/**
* The Binh (4) problem. The global feasible optimum is at {@code (3.0, 0.5)}.
* <p>
* Properties:
* <ul>
* <li>Connected Pareto set
* <li>Degenerate, concave Pareto front
* <li>Constrained
* </ul>
* <p>
* References:
* <ol>
* <li>Binh, T. T., and Korn, U. (1997). "Multiobjective Evolution Strategy
* with Linear and Nonlinear Constraints." Proc. of the 15th IMACS
* World Congress on Scientific Computation, Modeling and Applied
* Mathematics, pp. 357-362.
* <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 Binh4 extends AbstractProblem implements AnalyticalProblem {
/**
* Constructs the Binh (4) problem.
*/
public Binh4() {
super(2, 3, 2);
}
@Override
public void evaluate(Solution solution) {
double x = EncodingUtils.getReal(solution.getVariable(0));
double y = EncodingUtils.getReal(solution.getVariable(1));
double f1 = 1.5 - x*(1.0 - y);
double f2 = 2.25 - x*(1.0 - Math.pow(y, 2.0));
double f3 = 2.625 - x*(1.0 - Math.pow(y, 3.0));
double c1 = -Math.pow(x, 2.0) - Math.pow(y - 0.5, 2.0) + 9.0;
double c2 = Math.pow(x - 1.0, 2.0) + Math.pow(y - 0.5, 2.0) - 6.25;
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);
}
@Override
public Solution newSolution() {
Solution solution = new Solution(2, 3, 2);
solution.setVariable(0, EncodingUtils.newReal(-10.0, 10.0));
solution.setVariable(1, EncodingUtils.newReal(-10.0, 10.0));
return solution;
}
@Override
public Solution generate() {
Solution solution = newSolution();
EncodingUtils.setReal(solution.getVariable(0), 3.0);
EncodingUtils.setReal(solution.getVariable(1), 0.5);
evaluate(solution);
return solution;
}
}