    001package net.sf.cpsolver.ifs.algorithms;
    002
    003import java.text.DecimalFormat;
    004
    005import net.sf.cpsolver.ifs.heuristics.NeighbourSelection;
    006import net.sf.cpsolver.ifs.model.Model;
    007import net.sf.cpsolver.ifs.model.Neighbour;
    008import net.sf.cpsolver.ifs.model.Value;
    009import net.sf.cpsolver.ifs.model.Variable;
    010import net.sf.cpsolver.ifs.solution.Solution;
    011import net.sf.cpsolver.ifs.util.DataProperties;
    012import net.sf.cpsolver.ifs.util.JProf;
    013
    014/**
    015 * Great deluge. In each iteration, one of the given neighbourhoods is selected first,
    016 * then a neighbour is generated and it is accepted if the value of the new
    017 * solution is below certain bound. This bound is initialized to the
    018 * GreatDeluge.UpperBoundRate &times; value of the best solution ever found.
    019 * After each iteration, the bound is decreased by GreatDeluge.CoolRate (new
    020 * bound equals to old bound &times; GreatDeluge.CoolRate). If the bound gets
    021 * bellow GreatDeluge.LowerBoundRate &times; value of the best solution ever
    022 * found, it is changed back to GreatDeluge.UpperBoundRate &times; value of the
    023 * best solution ever found.
    024 * <br><br>
    025 * If there was no improvement found between the bounds, the new bounds are
    026 * changed to GreatDeluge.UpperBoundRate^2 and GreatDeluge.LowerBoundRate^2,
    027 * GreatDeluge.UpperBoundRate^3 and GreatDeluge.LowerBoundRate^3, etc. till
    028 * there is an improvement found. <br>
    029 * <br>
    030 * Custom neighbours can be set using GreatDeluge.Neighbours property that should
    031 * contain semicolon separated list of {@link NeighbourSelection}. By default,
    032 * each neighbour selection is selected with the same probability (each has 1 point in
    033 * a roulette wheel selection). It can be changed by adding &nbsp;@n at the end
    034 * of the name of the class, for example:<br>
    035 * <code>
    036 * GreatDeluge.Neighbours=net.sf.cpsolver.ifs.algorithms.neighbourhoods.RandomMove;net.sf.cpsolver.ifs.algorithms.neighbourhoods.RandomSwapMove@0.1
    037 * </code>
    038 * <br>
    039 * Selector RandomSwapMove is 10&times; less probable to be selected than other selectors.
    040 * When GreatDeluge.Random is true, all selectors are selected with the same probability, ignoring these weights.
    041 * <br><br>
    042 * When GreatDeluge.Update is true, {@link NeighbourSelector#update(Neighbour, long)} is called
    043 * after each iteration (on the selector that was used) and roulette wheel selection
    044 * that is using {@link NeighbourSelector#getPoints()} is used to pick a selector in each iteration.
    045 * See {@link NeighbourSelector} for more details.
    046 * <br>
    047 *
    048 * @version IFS 1.2 (Iterative Forward Search)<br>
    049 *          Copyright (C) 2014 Tomáš Müller<br>
    050 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
    051 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
    052 * <br>
    053 *          This library is free software; you can redistribute it and/or modify
    054 *          it under the terms of the GNU Lesser General Public License as
    055 *          published by the Free Software Foundation; either version 3 of the
    056 *          License, or (at your option) any later version. <br>
    057 * <br>
    058 *          This library is distributed in the hope that it will be useful, but
    059 *          WITHOUT ANY WARRANTY; without even the implied warranty of
    060 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
    061 *          Lesser General Public License for more details. <br>
    062 * <br>
    063 *          You should have received a copy of the GNU Lesser General Public
    064 *          License along with this library; if not see
    065 *          <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>.
    066 */
    067public class GreatDeluge<V extends Variable<V, T>, T extends Value<V, T>> extends NeighbourSearch<V, T> {
    068    private DecimalFormat sDF2 = new DecimalFormat("0.00");
    069    private DecimalFormat sDF5 = new DecimalFormat("0.00000");
    070    private double iBound = 0.0;
    071    private double iCoolRate = 0.9999999;
    072    private double iUpperBound;
    073    private double iUpperBoundRate = 1.05;
    074    private double iLowerBoundRate = 0.95;
    075    private int iMoves = 0;
    076    private int iAcceptedMoves = 0;
    077    private int iNrIdle = 0;
    078    private long iLastImprovingIter = 0;
    079    private double iBestValue = 0;
    080
    081    /**
    082     * Constructor. Following problem properties are considered:
    083     * <ul>
    084     * <li>GreatDeluge.CoolRate ... bound cooling rate (default 0.99999995)
    085     * <li>GreatDeluge.UpperBoundRate ... bound upper bound relative to best solution ever found (default 1.05)
    086     * <li>GreatDeluge.LowerBoundRate ... bound lower bound relative to best solution ever found (default 0.95)
    087     * <li>GreatDeluge.Neighbours ... semicolon separated list of classes implementing {@link NeighbourSelection}
    088     * <li>GreatDeluge.AdditionalNeighbours ... semicolon separated list of classes implementing {@link NeighbourSelection}
    089     * <li>GreatDeluge.Random ... when true, a neighbour selector is selected randomly
    090     * <li>GreatDeluge.Update ... when true, a neighbour selector is selected using {@link NeighbourSelector#getPoints()} weights (roulette wheel selection)
    091     * </ul>
    092     *
    093     * @param properties
    094     *            problem properties
    095     */
    096    public GreatDeluge(DataProperties properties) {
    097        super(properties);
    098        iCoolRate = properties.getPropertyDouble(getParameterBaseName() + ".CoolRate", iCoolRate);
    099        iUpperBoundRate = properties.getPropertyDouble(getParameterBaseName() + ".UpperBoundRate", iUpperBoundRate);
    100        iLowerBoundRate = properties.getPropertyDouble(getParameterBaseName() + ".LowerBoundRate", iLowerBoundRate);
    101    }
    102
    103    /** Accept the given neighbour if it does not worsen the current solution or when the new solution is below the bound */
    104    @Override
    105    protected boolean accept(Model<V, T> model, Neighbour<V, T> neighbour, double value, boolean lazy) {
    106        iMoves ++;
    107        if (value <= 0.0 || (lazy ? model.getTotalValue() : value + model.getTotalValue()) < iBound) {
    108            iAcceptedMoves ++;
    109            return true;
    110        }
    111        return false;
    112    }
    113
    114    /** Setup the bound */
    115    @Override
    116    protected void activate(Solution<V, T> solution) {
    117        super.activate(solution);
    118        iNrIdle = 0;
    119        iLastImprovingIter = 0;
    120        iBound = (solution.getBestValue() > 0.0 ? iUpperBoundRate * solution.getBestValue() : solution.getBestValue() / iUpperBoundRate);
    121        iUpperBound = iBound;
    122    }
    123
    124    /** Increment iteration count, update bound */
    125    @Override
    126    protected void incIteration(Solution<V, T> solution) {
    127        super.incIteration(solution);
    128        if (solution.getBestValue() >= 0.0)
    129            iBound *= iCoolRate;
    130        else
    131            iBound /= iCoolRate;
    132        if (iIter % 10000 == 0) {
    133            iLog.info("Iter=" + iIter / 1000 + "k, NonImpIter=" + sDF2.format((iIter - iLastImprovingIter) / 1000.0)
    134                    + "k, Speed=" + sDF2.format(1000.0 * iIter / (JProf.currentTimeMillis() - iT0)) + " it/s");
    135            iLog.info("Bound is " + sDF2.format(iBound) + ", " + "best value is " + sDF2.format(solution.getBestValue())
    136                    + " (" + sDF2.format(100.0 * iBound / solution.getBestValue()) + "%), " + "current value is "
    137                    + sDF2.format(solution.getModel().getTotalValue()) + " ("
    138                    + sDF2.format(100.0 * iBound / solution.getModel().getTotalValue()) + "%), " + "#idle=" + iNrIdle
    139                    + ", " + "Pacc=" + sDF5.format(100.0 * iAcceptedMoves / iMoves) + "%");
    140            logNeibourStatus();
    141            iAcceptedMoves = iMoves = 0;
    142        }
    143        double lowerBound = (solution.getBestValue() >= 0.0
    144                ? Math.pow(iLowerBoundRate, 1 + iNrIdle) * solution.getBestValue()
    145                : solution.getBestValue() / Math.pow(iLowerBoundRate, 1 + iNrIdle));
    146        if (iBound < lowerBound) {
    147            iNrIdle++;
    148            iBound = Math.max(solution.getBestValue() + 2.0,(solution.getBestValue() >= 0.0 ?
    149                    Math.pow(iUpperBoundRate, iNrIdle) * solution.getBestValue() :
    150                    solution.getBestValue() / Math.pow(iUpperBoundRate, iNrIdle)));
    151            iUpperBound = iBound;
    152            iProgress.setPhase("Great Deluge [" + (1 + iNrIdle) + "]...");
    153        }
    154        iProgress.setProgress(100 - Math.round(100.0 * (iBound - lowerBound) / (iUpperBound - lowerBound)));
    155        iMoves++;
    156    }
    157
    158    /** Update last improving iteration count */
    159    @Override
    160    public void bestSaved(Solution<V, T> solution) {
    161        if (Math.abs(iBestValue - solution.getBestValue()) >= 1.0) {
    162            iLastImprovingIter = iIter;
    163            iNrIdle = 0;
    164            iBestValue = solution.getBestValue();
    165        }
    166    }
    167
    168    @Override
    169    public String getParameterBaseName() { return "GreatDeluge"; }
    170}