/* 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.WFG;
import java.util.Arrays;
import org.moeaframework.core.PRNG;
/* This code is based on the Walking Fish Group implementation.
*
* Copyright 2005 The Walking Fish Group (WFG).
*
* This material is provided "as is", with no warranty expressed or implied.
* Any use is at your own risk. Permission to use or copy this software for
* any purpose is hereby granted without fee, provided this notice is
* retained on all copies. Permission to modify the code and to distribute
* modified code is granted, provided a notice that the code was modified is
* included with the above copyright notice.
*
* http://www.wfg.csse.uwa.edu.au/
*/
class Solutions {
/**
* Private constructor to prevent instantiation.
*/
private Solutions() {
super();
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the WFG1 problem with {@code k} position-related variables
* and {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the WFG1 problem
*/
public static double[] WFG_1_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = Math.pow(PRNG.nextDouble(), 50.0); // account for
// polynomial bias
}
// set distance parameters
for (int i = k; i < k + l; i++) {
result[i] = 0.35;
}
// scale to the correct domains
for (int i = 0; i < k + l; i++) {
result[i] *= 2.0 * (i + 1);
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the WFG2-WFG7 problems with {@code k} position-related
* variables and {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the WFG2-WFG7 problems
*/
public static double[] WFG_2_thru_7_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = PRNG.nextDouble();
}
// set distance parameters
for (int i = k; i < k + l; i++) {
result[i] = 0.35;
}
// scale to the correct domains
for (int i = 0; i < k + l; i++) {
result[i] *= 2.0 * (i + 1);
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the WFG8 problem with {@code k} position-related variables
* and {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the WFG8 problem
*/
public static double[] WFG_8_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = PRNG.nextDouble();
}
// set distance parameters
for (int i = k; i < k + l; i++) {
double[] w = new double[k + l];
Arrays.fill(w, 1.0);
double u = TransFunctions.r_sum(result, w);
double tmp1 = Math.abs(Math.floor(0.5 - u) + 0.98 / 49.98);
double tmp2 = 0.02 + 49.98 * (0.98 / 49.98 - (1.0 - 2.0 * u) * tmp1);
result[i] = Math.pow(0.35, Math.pow(tmp2, -1.0));
}
// scale to the correct domains
for (int i = 0; i < k + l; i++) {
result[i] *= 2.0 * (i + 1);
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the WFG9 problem with {@code k} position-related variables
* and {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the WFG9 problem
*/
public static double[] WFG_9_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = PRNG.nextDouble();
}
// set distance parameters
result[k + l - 1] = 0.35; // the last distance parameter is easy
for (int i = k + l - 2; i >= k; i--) {
double[] result_sub = new double[k + l - i - 1];
for (int j = i + 1; j < k + l; j++) {
result_sub[j - i - 1] = result[j];
}
double[] w = new double[result_sub.length];
Arrays.fill(w, 1.0);
double tmp1 = TransFunctions.r_sum(result_sub, w);
result[i] = Math.pow(0.35, Math.pow(0.02 + 1.96 * tmp1, -1.0));
}
// scale to the correct domains
for (int i = 0; i < k + l; i++) {
result[i] *= 2.0 * (i + 1);
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the I1 problem with {@code k} position-related variables and
* {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the I1 problem
*/
public static double[] I1_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = PRNG.nextDouble();
}
// set distance parameters
for (int i = k; i < k + l; i++) {
result[i] = 0.35;
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the I2 problem with {@code k} position-related variables and
* {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the I2 problem
*/
public static double[] I2_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = PRNG.nextDouble();
}
// set distance parameters
result[k + l - 1] = 0.35; // the last distance parameter is easy
for (int i = k + l - 2; i >= k; i--) {
double[] result_sub = new double[k + l - i - 1];
for (int j = i + 1; j < k + l; j++) {
result_sub[j - i - 1] = result[j];
}
double[] w = new double[result_sub.length];
Arrays.fill(w, 1.0);
double tmp1 = TransFunctions.r_sum(result_sub, w);
result[i] = Math.pow(0.35, Math.pow(0.02 + 1.96 * tmp1, -1.0));
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the I3 problem with {@code k} position-related variables and
* {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the I3 problem
*/
public static double[] I3_random_soln(int k, int l) {
double[] result = new double[k + l];
// set position parameters
for (int i = 0; i < k; i++) {
result[i] = PRNG.nextDouble();
}
// set distance parameters
for (int i = k; i < k + l; i++) {
double[] w = new double[k + l];
Arrays.fill(w, 1.0);
double u = TransFunctions.r_sum(result, w);
double tmp1 = Math.abs(Math.floor(0.5 - u) + 0.98 / 49.98);
double tmp2 = 0.02 + 49.98 * (0.98 / 49.98 - (1.0 - 2.0 * u) * tmp1);
result[i] = Math.pow(0.35, Math.pow(tmp2, -1.0));
}
return result;
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the I4 problem with {@code k} position-related variables and
* {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the I4 problem
*/
public static double[] I4_random_soln(int k, int l) {
return I1_random_soln(k, l);
}
/**
* Returns the decision variables of a randomly-generated Pareto optimal
* solution to the I5 problem with {@code k} position-related variables and
* {@code l} distance-related variables.
*
* @param k the number of position-related parameters
* @param l the number of distance-related parameters
* @return the decision variables of a randomly-generated Pareto optimal
* solution to the I5 problem
*/
public static double[] I5_random_soln(int k, int l) {
return I3_random_soln(k, l);
}
}