/* 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; } }