/*******************************************************************************
* Copyright (C) 2014 Stefan Schroeder
*
* 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 3.0 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, see <http://www.gnu.org/licenses/>.
******************************************************************************/
package jsprit.core.algorithm.acceptor;
import jsprit.core.algorithm.VehicleRoutingAlgorithm;
import jsprit.core.algorithm.listener.AlgorithmStartsListener;
import jsprit.core.algorithm.listener.IterationStartsListener;
import jsprit.core.problem.VehicleRoutingProblem;
import jsprit.core.problem.solution.VehicleRoutingProblemSolution;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import java.util.Collection;
/**
* ThresholdAcceptance-Function defined by Schrimpf et al. (2000).
*
* <p>The <b>idea</b> can be described as follows: Most problems do not only have one unique minimum (maximum) but
* a number of local minima (maxima). To avoid to get stuck in a local minimum at the beginning of a search
* this threshold-acceptance function accepts also worse solution at the beginning (in contrary to a greedy
* approach which only accepts better solutions), and converges to a greedy approach at the end. <br>
* The difficulty is to define (i) an appropriate initial threshold and (ii) a corresponding function describing
* how the threshold converges to zero, i.e. the greedy threshold.
*
* <p>ad i) The initial threshold is determined by a random walk through the search space.
* The random walk currently runs with the following algorithm: src/main/resources/randomWalk.xml. It runs
* as long as it is specified in nuOfWarmupIterations. In the first iteration or walk respectively the algorithm generates a solution.
* This solution in turn is the basis of the next walk yielding to another solution value ... and so on.
* Each solution value is memorized since the initial threshold is essentially a function of the standard deviation of these solution values.
* To be more precise: initial threshold = stddev(solution values) / 2.
*
* <p>ad ii) The threshold of iteration i is determined as follows:
* threshold(i) = initialThreshold * Math.exp(-Math.log(2) * (i / nuOfTotalIterations) / alpha)
* To get a better understanding of the threshold-function go to Wolfram Alpha and plot the following line
* (just copy and paste it into Wolfram's console: <a href="https://www.wolframalpha.com/">www.wolframalpha.com</a>):
* <p>100. * exp(-log(2)* (x/1000) / 0.1) (x from 0 to 1000) (y from 0 to 100)
* <p>with <br>
* initialThreshold = 100<br>
* nuOfTotalIter = 1000<br>
* alpha = 0.1<br>
* x corresponds to i iterations and<br>
* y to the threshold(i)
*
* <p>Gerhard Schrimpf, Johannes Schneider, Hermann Stamm- Wilbrandt, and Gunter Dueck (2000).
* Record breaking optimization results using the ruin and recreate principle.
* Journal of Computational Physics, 159(2):139 – 171, 2000. ISSN 0021-9991. doi: 10.1006/jcph.1999. 6413.
* URL http://www.sciencedirect.com/science/article/ pii/S0021999199964136
*
*
* @author schroeder
*
*/
public class SchrimpfAcceptance implements SolutionAcceptor, IterationStartsListener, AlgorithmStartsListener{
private static Logger logger = LogManager.getLogger(SchrimpfAcceptance.class.getName());
private final double alpha;
private int maxIterations = 1000;
private int currentIteration = 0;
private double initialThreshold = 0.0;
private final int solutionMemory;
public SchrimpfAcceptance(int solutionMemory, double alpha){
this.alpha = alpha;
this.solutionMemory = solutionMemory;
logger.info("initialise " + this);
}
@Override
public boolean acceptSolution(Collection<VehicleRoutingProblemSolution> solutions, VehicleRoutingProblemSolution newSolution) {
boolean solutionAccepted = false;
if (solutions.size() < solutionMemory) {
solutions.add(newSolution);
solutionAccepted = true;
} else {
VehicleRoutingProblemSolution worst = null;
double threshold = getThreshold(currentIteration);
for(VehicleRoutingProblemSolution solutionInMemory : solutions){
if(worst == null) worst = solutionInMemory;
else if(solutionInMemory.getCost() > worst.getCost()) worst = solutionInMemory;
}
if(worst == null){
solutions.add(newSolution);
solutionAccepted = true;
}
else if(newSolution.getCost() < worst.getCost() + threshold){
solutions.remove(worst);
solutions.add(newSolution);
solutionAccepted = true;
}
}
return solutionAccepted;
}
@Override
public String toString() {
return "[name=SchrimpfAcceptance][alpha="+alpha+"]";
}
private double getThreshold(int iteration) {
double scheduleVariable = (double) iteration / (double) maxIterations;
return initialThreshold * Math.exp(-1. * Math.log(2) * scheduleVariable / alpha);
}
@SuppressWarnings("UnusedDeclaration")
public double getInitialThreshold(){
return initialThreshold;
}
/**
* Sets initial threshold.
* <p>Note that if initial threshold has been set, automatic generation of initial threshold is disabled.
*
* @param initialThreshold the initialThreshold to set
*/
public void setInitialThreshold(double initialThreshold) {
this.initialThreshold = initialThreshold;
}
@Override
public void informAlgorithmStarts(VehicleRoutingProblem problem, VehicleRoutingAlgorithm algorithm, Collection<VehicleRoutingProblemSolution> solutions) {
reset();
this.maxIterations = algorithm.getMaxIterations();
}
private void reset() {
currentIteration = 0;
}
@Override
public void informIterationStarts(int i, VehicleRoutingProblem problem, Collection<VehicleRoutingProblemSolution> solutions) {
currentIteration = i;
}
}