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/* 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 .
 */
package org.moeaframework.problem.misc;

import org.moeaframework.core.Solution;
import org.moeaframework.core.variable.RealVariable;
import org.moeaframework.problem.AbstractProblem;

/**
 * The Lis problem.
 * 

* Properties: *

    *
  • Disconnected Pareto set *
  • Disconnected and concave Pareto front *
*

* References: *

    *
  1. Lis, J. and Eiben, A. E. (1996). "A Multi-Sexual Genetic Algorithm for * Multiobjective Optimization." Proceedings of the IEEE International * Conference on Evolutionary Computation, 59-64. *
  2. Van Veldhuizen, D. A (1999). "Multiobjective Evolutionary Algorithms: * Classifications, Analyses, and New Innovations." Air Force Institute * of Technology, Ph.D. Thesis, Appendix B. *
*/ public class Lis extends AbstractProblem { /** * Constructs the Lis problem. */ public Lis() { super(2, 2); } @Override public void evaluate(Solution solution) { double x = ((RealVariable)solution.getVariable(0)).getValue(); double y = ((RealVariable)solution.getVariable(1)).getValue(); double f1 = Math.pow(Math.pow(x, 2.0) + Math.pow(y, 2.0), 1.0/8.0); double f2 = Math.pow(Math.pow(x-0.5, 2.0) + Math.pow(y-0.5, 2.0), 1.0/4.0); solution.setObjective(0, f1); solution.setObjective(1, f2); } @Override public Solution newSolution() { Solution solution = new Solution(2, 2); solution.setVariable(0, new RealVariable(-5.0, 10.0)); solution.setVariable(1, new RealVariable(-5.0, 10.0)); return solution; } }




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