    001package org.cpsolver.ifs.heuristics;
    002
    003import java.util.ArrayList;
    004import java.util.Collection;
    005import java.util.Iterator;
    006import java.util.List;
    007import java.util.Set;
    008
    009import org.cpsolver.ifs.assignment.Assignment;
    010import org.cpsolver.ifs.extension.ConflictStatistics;
    011import org.cpsolver.ifs.extension.Extension;
    012import org.cpsolver.ifs.extension.MacPropagation;
    013import org.cpsolver.ifs.extension.ViolatedInitials;
    014import org.cpsolver.ifs.model.Model;
    015import org.cpsolver.ifs.model.Value;
    016import org.cpsolver.ifs.model.Variable;
    017import org.cpsolver.ifs.solution.Solution;
    018import org.cpsolver.ifs.solver.Solver;
    019import org.cpsolver.ifs.util.DataProperties;
    020import org.cpsolver.ifs.util.ToolBox;
    021
    022
    023/**
    024 * General implementation of value selection criterion. <br>
    025 * <br>
    026 * Value selection criterion is based on weighted sum of various criteria. It
    027 * also allows random walk technique and tabu search. <br>
    028 * Parameters: <br>
    029 * <table border='1'><caption>Related Solver Parameters</caption>
    030 * <tr>
    031 * <th>Parameter</th>
    032 * <th>Type</th>
    033 * <th>Comment</th>
    034 * </tr>
    035 * <tr>
    036 * <td>General.MPP</td>
    037 * <td>{@link Boolean}</td>
    038 * <td>if true, MPP is being solved</td>
    039 * </tr>
    040 * <tr>
    041 * <td>Value.MPPLimit</td>
    042 * <td>{@link Integer}</td>
    043 * <td>MPP: limitation of the number of allowed perturbations. If a solution
    044 * within this limit is gound, it is decreased.</td>
    045 * </tr>
    046 * <tr>
    047 * <td>Value.InitialSelectionProb</td>
    048 * <td>{@link Double}</td>
    049 * <td>MPP: probability of selection of the initial value</td>
    050 * </tr>
    051 * <tr>
    052 * <td>Value.RandomWalkProb</td>
    053 * <td>{@link Double}</td>
    054 * <td>Random Walk: probability of selection of a value randomly among all the
    055 * values</td>
    056 * </tr>
    057 * <tr>
    058 * <td>Value.Tabu</td>
    059 * <td>{@link Integer}</td>
    060 * <td>Tabu Search: length of the tabu-list</td>
    061 * </tr>
    062 * <tr>
    063 * <td>Value.GoodSelectionProb</td>
    064 * <td>{@link Double}</td>
    065 * <td>In case of {@link MacPropagation}, with this probability (1.0 means
    066 * always), the selection is made only among good values (not removed from the
    067 * domain).</td>
    068 * </tr>
    069 * </table>
    070 * <br>
    071 * Following weights are used in the weighted sum (computed for all values). The
    072 * value with the lowest weighted sum is selected. If there are more than one of
    073 * such values, one of them is selected randomly. <br>
    074 * <table border='1'><caption>Related Solver Parameters</caption>
    075 * <tr>
    076 * <th>Parameter</th>
    077 * <th>Type</th>
    078 * <th>Comment</th>
    079 * </tr>
    080 * <tr>
    081 * <td>Value.WeightDeltaInitialAssignments</td>
    082 * <td>{@link Double}</td>
    083 * <td>MPP: Difference in the number of assigned initial values if the value is
    084 * assigned to the variable (weighted by this
    085 * Value.WeightDeltaInitialAssignments): -1 if the value is initial, 0
    086 * otherwise, increased by the number of initial values assigned to variables
    087 * with hard conflicts with the value</td>
    088 * </tr>
    089 * <tr>
    090 * <td>Value.WeightWeightedConflicts</td>
    091 * <td>{@link Double}</td>
    092 * <td>When {@link ConflictStatistics} is used: weighted number of conflicting
    093 * variables</td>
    094 * </tr>
    095 * <tr>
    096 * <td>Value.WeightPotentialConflicts</td>
    097 * <td>{@link Double}</td>
    098 * <td>When {@link ConflictStatistics} is used: weighted number of potentially
    099 * conflicting variables</td>
    100 * </tr>
    101 * <tr>
    102 * <td>Value.WeightConflicts</td>
    103 * <td>{@link Double}</td>
    104 * <td>Number of conflicting variables {@link Model#conflictValues(Assignment, Value)}.</td>
    105 * </tr>
    106 * <tr>
    107 * <td>Value.WeightValue</td>
    108 * <td>{@link Double}</td>
    109 * <td>Value {@link Value#toDouble(Assignment)}</td>
    110 * </tr>
    111 * </table>
    112 *
    113 * @see VariableSelection
    114 * @see Solver
    115 *
    116 * @author  Tomáš Müller
    117 * @version IFS 1.3 (Iterative Forward Search)<br>
    118 *          Copyright (C) 2006 - 2014 Tomáš Müller<br>
    119 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
    120 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
    121 * <br>
    122 *          This library is free software; you can redistribute it and/or modify
    123 *          it under the terms of the GNU Lesser General Public License as
    124 *          published by the Free Software Foundation; either version 3 of the
    125 *          License, or (at your option) any later version. <br>
    126 * <br>
    127 *          This library is distributed in the hope that it will be useful, but
    128 *          WITHOUT ANY WARRANTY; without even the implied warranty of
    129 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
    130 *          Lesser General Public License for more details. <br>
    131 * <br>
    132 *          You should have received a copy of the GNU Lesser General Public
    133 *          License along with this library; if not see <a href='http://www.gnu.org/licenses'>http://www.gnu.org/licenses</a>.
    134 *
    135 * @param <V> Variable
    136 * @param <T> Value
    137 **/
    138public class GeneralValueSelection<V extends Variable<V, T>, T extends Value<V, T>> implements ValueSelection<V, T> {
    139    private double iRandomWalkProb = 0.0;
    140    private double iInitialSelectionProb = 0.0;
    141    private double iGoodSelectionProb = 0.0;
    142    private int iMPPLimit = -1;
    143
    144    private double iWeightDeltaInitialAssignment = 0.0;
    145    private double iWeightPotentialConflicts = 0.0;
    146    private double iWeightWeightedCoflicts = 0.0;
    147    private double iWeightCoflicts = 1.0;
    148    private double iWeightValue = 0.0;
    149
    150    protected int iTabuSize = 0;
    151    protected ArrayList<T> iTabu = null;
    152    protected int iTabuPos = 0;
    153
    154    private boolean iMPP = false;
    155    private ConflictStatistics<V, T> iStat = null;
    156    private MacPropagation<V, T> iProp = null;
    157    private ViolatedInitials<V, T> iViolatedInitials = null;
    158
    159    public GeneralValueSelection() {
    160    }
    161
    162    /**
    163     * Constructor
    164     *
    165     * @param properties
    166     *            input configuration
    167     */
    168    public GeneralValueSelection(DataProperties properties) {
    169        iMPP = properties.getPropertyBoolean("General.MPP", false);
    170        if (iMPP) {
    171            iMPPLimit = properties.getPropertyInt("Value.MPPLimit", -1);
    172            iInitialSelectionProb = properties.getPropertyDouble("Value.InitialSelectionProb", 0.75);
    173            iWeightDeltaInitialAssignment = properties.getPropertyDouble("Value.WeightDeltaInitialAssignments", 0.0);
    174        }
    175        iGoodSelectionProb = properties.getPropertyDouble("Value.GoodSelectionProb", 0.00);
    176        iWeightWeightedCoflicts = properties.getPropertyDouble("Value.WeightWeightedConflicts", 1.0);
    177        iWeightPotentialConflicts = properties.getPropertyDouble("Value.WeightPotentialConflicts", 0.0);
    178
    179        iRandomWalkProb = properties.getPropertyDouble("Value.RandomWalkProb", 0.0);
    180        iWeightCoflicts = properties.getPropertyDouble("Value.WeightConflicts", 1.0);
    181        iWeightValue = properties.getPropertyDouble("Value.WeightValue", 0.0);
    182        iTabuSize = properties.getPropertyInt("Value.Tabu", 0);
    183        if (iTabuSize > 0)
    184            iTabu = new ArrayList<T>(iTabuSize);
    185    }
    186
    187    /** Initialization */
    188    @Override
    189    public void init(Solver<V, T> solver) {
    190        for (Extension<V, T> extension : solver.getExtensions()) {
    191            if (ConflictStatistics.class.isInstance(extension))
    192                iStat = (ConflictStatistics<V, T>) extension;
    193            if (MacPropagation.class.isInstance(extension))
    194                iProp = (MacPropagation<V, T>) extension;
    195            if (ViolatedInitials.class.isInstance(extension))
    196                iViolatedInitials = (ViolatedInitials<V, T>) extension;
    197        }
    198    }
    199
    200    /** Value selection */
    201    @Override
    202    public T selectValue(Solution<V, T> solution, V selectedVariable) {
    203        if (iMPP) {
    204            if (selectedVariable.getInitialAssignment() != null) {
    205                if (solution.getModel().variables().size() == solution.getAssignment().nrAssignedVariables()) {
    206                    if (solution.getModel().perturbVariables(solution.getAssignment()).size() <= iMPPLimit)
    207                        iMPPLimit = solution.getModel().perturbVariables(solution.getAssignment()).size() - 1;
    208                }
    209                if (iMPPLimit >= 0 && solution.getModel().perturbVariables(solution.getAssignment()).size() > iMPPLimit)
    210                    return selectedVariable.getInitialAssignment();
    211                if (selectedVariable.getInitialAssignment() != null && ToolBox.random() <= iInitialSelectionProb)
    212                    return selectedVariable.getInitialAssignment();
    213            }
    214        }
    215
    216        T oldValue = solution.getAssignment().getValue(selectedVariable);
    217        List<T> values = selectedVariable.values(solution.getAssignment());
    218        if (ToolBox.random() <= iRandomWalkProb)
    219            return ToolBox.random(values);
    220        if (iProp != null && oldValue == null && ToolBox.random() <= iGoodSelectionProb) {
    221            Collection<T> goodValues = iProp.goodValues(solution.getAssignment(), selectedVariable);
    222            if (!goodValues.isEmpty())
    223                values = new ArrayList<T>(goodValues);
    224        }
    225        if (values.size() == 1)
    226            return values.get(0);
    227
    228        List<T> bestValues = null;
    229        double bestWeightedSum = 0;
    230
    231        for (T value : values) {
    232            if (iTabu != null && iTabu.contains(value))
    233                continue;
    234            if (oldValue != null && oldValue.equals(value))
    235                continue;
    236
    237            Collection<T> conf = solution.getModel().conflictValues(solution.getAssignment(), value);
    238            if (conf.contains(value))
    239                continue;
    240
    241            double weightedConflicts = (iStat == null || iWeightWeightedCoflicts == 0.0 ? 0.0 : iStat.countRemovals(solution.getIteration(), conf, value));
    242            double potentialConflicts = (iStat == null || iWeightPotentialConflicts == 0.0 ? 0.0 : iStat.countPotentialConflicts(solution.getAssignment(), solution.getIteration(), value, 3));
    243
    244            long deltaInitialAssignments = 0;
    245            if (iMPP && iWeightDeltaInitialAssignment != 0.0) {
    246                if (iViolatedInitials != null) {
    247                    Set<T> violations = iViolatedInitials.getViolatedInitials(value);
    248                    if (violations != null) {
    249                        for (T aValue : violations) {
    250                            T aOld = solution.getAssignment().getValue(aValue.variable());
    251                            if (aOld == null || aOld.equals(aValue))
    252                                deltaInitialAssignments += 2;
    253                        }
    254                    }
    255                }
    256                for (Iterator<T> it1 = conf.iterator(); it1.hasNext();) {
    257                    T aValue = it1.next();
    258                    if (aValue.variable().getInitialAssignment() != null)
    259                        deltaInitialAssignments--;
    260                }
    261                if (selectedVariable.getInitialAssignment() != null
    262                        && !selectedVariable.getInitialAssignment().equals(value)) {
    263                    deltaInitialAssignments++;
    264                }
    265                if (iMPPLimit >= 0 && (solution.getModel().perturbVariables(solution.getAssignment()).size() + deltaInitialAssignments) > iMPPLimit)
    266                    continue;
    267            }
    268
    269            double weightedSum = (iWeightDeltaInitialAssignment * deltaInitialAssignments)
    270                    + (iWeightPotentialConflicts * potentialConflicts) + (iWeightWeightedCoflicts * weightedConflicts)
    271                    + (iWeightCoflicts * conf.size()) + (iWeightValue * value.toDouble(solution.getAssignment()));
    272
    273            if (bestValues == null || bestWeightedSum > weightedSum) {
    274                bestWeightedSum = weightedSum;
    275                if (bestValues == null)
    276                    bestValues = new ArrayList<T>();
    277                else
    278                    bestValues.clear();
    279                bestValues.add(value);
    280            } else {
    281                if (bestWeightedSum == weightedSum)
    282                    bestValues.add(value);
    283            }
    284        }
    285
    286        T selectedValue = (bestValues == null ? null : ToolBox.random(bestValues));
    287        if (selectedValue == null)
    288            selectedValue = ToolBox.random(values);
    289        if (iTabu != null) {
    290            if (iTabu.size() == iTabuPos)
    291                iTabu.add(selectedValue);
    292            else
    293                iTabu.set(iTabuPos, selectedValue);
    294            iTabuPos = (iTabuPos + 1) % iTabuSize;
    295        }
    296        return (bestValues == null ? null : selectedValue);
    297    }
    298
    299}