// Catalano Genetic Library
// The Catalano Framework
//
// Copyright © Diego Catalano, 2012-2018
// diego.catalano at live.com
//
// Copyright (c) 2015, Yarpiz (www.yarpiz.com)
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in
// the documentation and/or other materials provided with the distribution
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// This library 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 2.1 of the License, or (at your option) any later version.
//
// This library 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 this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
//
package Catalano.Evolutionary.Metaheuristics.Monoobjective;
import Catalano.Core.ArraysUtil;
import Catalano.Core.DoubleRange;
import Catalano.Math.Matrix;
import Catalano.Math.Tools;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
/**
* Simulated Annealing for Continuous space domain.
* @author Diego Catalano
*/
public class RealCodedSimulatedAnnealing extends BaseEvolutionaryOptimization {
private int maxSub;
private double t0;
private double alpha;
private int move;
private double mu;
private double sigma;
/**
* Get maximum sub iterations.
* @return Maximum sub iterations.
*/
public int getMaxSubIterations() {
return maxSub;
}
/**
* Set maximum sub iterations.
* @param maxSub Maximum sub iterations.
*/
public void setMaxSubIterations(int maxSub) {
this.maxSub = maxSub;
}
/**
* Get initial temperature.
* @return Initial temperature.
*/
public double getInitialTemperature() {
return t0;
}
/**
* Set initial temperature.
* @param t0 Initial temperature.
*/
public void setInitialTemperature(double t0) {
this.t0 = t0;
}
/**
* Get temperature reduction.
* @return Temperature reduction.
*/
public double getTemperatureReduction() {
return alpha;
}
/**
* Set temperature reduction.
* @param alpha Temperature reduction.
*/
public void setTemperatureReduction(double alpha) {
this.alpha = alpha;
}
/**
* Get number of neighbors.
* @return Number of neighbors.
*/
public int getNumberOfNeighbors() {
return move;
}
/**
* Set number of neighbors.
* @param move Number of neighbors.
*/
public void setNumberOfNeighbors(int move) {
this.move = move;
}
/**
* Get mutation rate.
* @return Mutation rate.
*/
public double getMutationRate() {
return mu;
}
/**
* Set mutation rate.
* @param mu Mutation rate.
*/
public void setMutationRate(double mu) {
this.mu = mu;
}
/**
* Get mutation range.
* @return Mutation range.
*/
public double getMutationRange() {
return sigma;
}
/**
* Set mutation range.
* @param sigma Mutation range.
*/
public void setMutationRange(double sigma) {
this.sigma = sigma;
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
*/
public RealCodedSimulatedAnnealing() {
this(10,1000);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population
* @param iterations Iterations.
*/
public RealCodedSimulatedAnnealing(int population, int iterations){
this(population, iterations, 20);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population.
* @param iterations Iterations.
* @param maxSub Maximum sub iterations.
*/
public RealCodedSimulatedAnnealing(int population, int iterations, int maxSub){
this(population, iterations, maxSub, 0.1);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population.
* @param iterations Iterations.
* @param maxSub Maximum sub iterations.
* @param t0 Initial temperature.
*/
public RealCodedSimulatedAnnealing(int population, int iterations, int maxSub, double t0){
this(population, iterations, maxSub, t0, 0.99);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population.
* @param iterations Iterations.
* @param maxSub Maximum sub iterations.
* @param t0 Initial temperature.
* @param alpha Temperature reduction rate.
*/
public RealCodedSimulatedAnnealing(int population, int iterations, int maxSub, double t0, double alpha){
this(population, iterations, maxSub, t0, alpha, 5);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population.
* @param iterations Iterations.
* @param maxSub Maximum sub iterations.
* @param t0 Initial temperature.
* @param alpha Temperature reduction rate.
* @param move Number of neighbors per individual.
*/
public RealCodedSimulatedAnnealing(int population, int iterations, int maxSub, double t0, double alpha, int move){
this(population, iterations, maxSub, t0, alpha, move, 0.5);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population.
* @param iterations Iterations.
* @param maxSub Maximum sub iterations.
* @param t0 Initial temperature.
* @param alpha Temperature reduction rate.
* @param move Number of neighbors per individual.
* @param mu Mutation rate.
*/
public RealCodedSimulatedAnnealing(int population, int iterations, int maxSub, double t0, double alpha, int move, double mu){
this(population, iterations, maxSub, t0, alpha, move, mu, 0.1);
}
/**
* Initialize a new instance of the RealCodedSimulatedAnnealing class.
* @param population Population.
* @param iterations Iterations.
* @param maxSub Maximum sub iterations.
* @param t0 Initial temperature.
* @param alpha Temperature reduction rate.
* @param move Number of neighbors per individual.
* @param mu Mutation rate.
* @param sigma Mutation range.
*/
public RealCodedSimulatedAnnealing(int population, int iterations, int maxSub, double t0, double alpha, int move, double mu, double sigma) {
this.populationSize = population;
this.generations = iterations;
this.maxSub = maxSub;
this.t0 = t0;
this.alpha = alpha;
this.move = move;
this.mu = mu;
this.sigma = sigma;
}
@Override
public void Compute(IObjectiveFunction function, List boundConstraint) {
minError = Double.MAX_VALUE;
nEvals = 0;
Random rand = new Random();
//Generate the individuals
double[][] pop = new double[populationSize][boundConstraint.size()];
for (int i = 0; i < pop.length; i++) {
pop[i] = Matrix.UniformRandom(boundConstraint);
}
//Compute fitness
double[] fitness = new double[pop.length];
for (int i = 0; i < fitness.length; i++)
fitness[i] = function.Compute(pop[i]);
//Initialize temp
double t = t0;
for (int i = 0; i < generations; i++) {
double[][] newPop = new double[pop.length * move][pop[0].length];
double[] newFitness = new double[newPop.length];
//Compute mutation range by sigma
double[] sigmaRange = new double[boundConstraint.size()];
for (int m = 0; m < sigmaRange.length; m++) {
DoubleRange range = boundConstraint.get(m);
sigmaRange[m] = sigma * (range.getMax() - range.getMin());
}
for (int j = 0; j < maxSub; j++) {
int index = 0;
for (int k = 0; k < pop.length; k++) {
for (int l = 0; l < move; l++) {
//Mutate
newPop[index] = Mutate(pop[k], mu, sigmaRange, boundConstraint, rand);
newFitness[index] = function.Compute(newPop[index]);
index++;
nEvals++;
}
}
//Sort
int[] order = ArraysUtil.Argsort(newFitness, true);
for (int p = 0; p < pop.length; p++) {
if(newFitness[order[p]] <= fitness[p]){
pop[p] = Arrays.copyOf(newPop[order[p]], newPop[0].length);
fitness[p] = newFitness[order[p]];
}
else{
double delta = (newFitness[order[p]] - fitness[p]) / fitness[p];
double pr = Math.exp(-delta/t);
if(rand.nextDouble() <= pr){
pop[p] = Arrays.copyOf(newPop[order[p]], newPop[0].length);
fitness[p] = newFitness[order[p]];
}
}
if(fitness[p] <= minError){
minError = fitness[p];
best = Arrays.copyOf(pop[p], pop[p].length);
}
}
}
//Update Temp.
t *= alpha;
sigma *= 0.98;
if(listener != null)
listener.onIteration(i+1, minError);
}
}
private double[] Mutate(double[] solution, double mu, double[] sigmaRange, List boundConstraint, Random rand){
double[] v = Matrix.UniformRandom(0, 1, solution.length);
List lst = new ArrayList();
for (int i = 0; i < v.length; i++) {
if(v[i] <= mu) lst.add(i);
}
v = Arrays.copyOf(solution, solution.length);
for (Integer i : lst) {
v[i] = solution[i] + sigmaRange[i] * rand.nextGaussian();
v[i] = Tools.Clamp(v[i], boundConstraint.get(i));
}
return v;
}
@Override
public double getError() {
return minError;
}
}
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