/* 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 Schaffer (2) problem. The Schaffer (2) problem is univariate, with the
* optimum defined by {@code 1 <= x <= 2} and {@code 4 <= x <= 5}.
* <p>
* Properties:
* <ul>
* <li>Disconnected Pareto set
* <li>Disconnected Pareto front
* </ul>
* <p>
* References:
* <ol>
* <li>Schaffer, J. D. (1984). "Some Experiments in Machine Learning using
* Vector Evaluated Genetic Algorithms." Ph.D. Thesis, Vanderbilt
* University, Nashville, USA.
* <li>Schaffer, J. D. (1985). "Multiple Objective Optimization with Vector
* Evaluated Genetic Algorithms." Genetic Algorithms and Their
* Applications: Proceedings of the First International Conference on
* Genetic Algorithms, pp. 93-100.
* <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 Schaffer2 extends AbstractProblem implements AnalyticalProblem {
/**
* Constructs the Schaffer (2) problem.
*/
public Schaffer2() {
super(1, 2);
}
@Override
public void evaluate(Solution solution) {
double x = EncodingUtils.getReal(solution.getVariable(0));
if (x <= 1.0) {
solution.setObjective(0, -x);
} else if (x <= 3.0) {
solution.setObjective(0, -2.0 + x);
} else if (x <= 4.0) {
solution.setObjective(0, 4.0 - x);
} else {
solution.setObjective(0, -4.0 + x);
}
solution.setObjective(1, Math.pow(x - 5.0, 2.0));
}
@Override
public Solution newSolution() {
Solution solution = new Solution(1, 2);
solution.setVariable(0, EncodingUtils.newReal(-5.0, 10.0));
return solution;
}
@Override
public Solution generate() {
Solution solution = newSolution();
if (PRNG.nextBoolean()) {
EncodingUtils.setReal(solution.getVariable(0),
PRNG.nextDouble(1.0, 2.0));
} else {
EncodingUtils.setReal(solution.getVariable(0),
PRNG.nextDouble(4.0, 5.0));
}
evaluate(solution);
return solution;
}
}