001package net.sf.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 net.sf.cpsolver.ifs.extension.ConflictStatistics;
010import net.sf.cpsolver.ifs.extension.Extension;
011import net.sf.cpsolver.ifs.extension.MacPropagation;
012import net.sf.cpsolver.ifs.extension.ViolatedInitials;
013import net.sf.cpsolver.ifs.model.Model;
014import net.sf.cpsolver.ifs.model.Value;
015import net.sf.cpsolver.ifs.model.Variable;
016import net.sf.cpsolver.ifs.solution.Solution;
017import net.sf.cpsolver.ifs.solver.Solver;
018import net.sf.cpsolver.ifs.util.DataProperties;
019import net.sf.cpsolver.ifs.util.ToolBox;
020
021/**
022 * General implementation of value selection criterion. <br>
023 * <br>
024 * Value selection criterion is based on weighted sum of various criteria. It
025 * also allows random walk technique and tabu search. <br>
026 * Parameters: <br>
027 * <table border='1'>
028 * <tr>
029 * <th>Parameter</th>
030 * <th>Type</th>
031 * <th>Comment</th>
032 * </tr>
033 * <tr>
034 * <td>General.MPP</td>
035 * <td>{@link Boolean}</td>
036 * <td>if true, MPP is being solved</td>
037 * </tr>
038 * <tr>
039 * <td>Value.MPPLimit</td>
040 * <td>{@link Integer}</td>
041 * <td>MPP: limitation of the number of allowed perturbations. If a solution
042 * within this limit is gound, it is decreased.</td>
043 * </tr>
044 * <tr>
045 * <td>Value.InitialSelectionProb</td>
046 * <td>{@link Double}</td>
047 * <td>MPP: probability of selection of the initial value</td>
048 * </tr>
049 * <tr>
050 * <td>Value.RandomWalkProb</td>
051 * <td>{@link Double}</td>
052 * <td>Random Walk: probability of selection of a value randomly among all the
053 * values</td>
054 * </tr>
055 * <tr>
056 * <td>Value.Tabu</td>
057 * <td>{@link Integer}</td>
058 * <td>Tabu Search: length of the tabu-list</td>
059 * </tr>
060 * <tr>
061 * <td>Value.GoodSelectionProb</td>
062 * <td>{@link Double}</td>
063 * <td>In case of {@link MacPropagation}, with this probability (1.0 means
064 * always), the selection is made only among good values (not removed from the
065 * domain).</td>
066 * </tr>
067 * </table>
068 * <br>
069 * Following weights are used in the weighted sum (computed for all values). The
070 * value with the lowest weighted sum is selected. If there are more than one of
071 * such values, one of them is selected randomly. <br>
072 * <table border='1'>
073 * <tr>
074 * <th>Parameter</th>
075 * <th>Type</th>
076 * <th>Comment</th>
077 * </tr>
078 * <tr>
079 * <td>Value.WeightDeltaInitialAssignments</td>
080 * <td>{@link Double}</td>
081 * <td>MPP: Difference in the number of assigned initial values if the value is
082 * assigned to the variable (weighted by this
083 * Value.WeightDeltaInitialAssignments): -1 if the value is initial, 0
084 * otherwise, increased by the number of initial values assigned to variables
085 * with hard conflicts with the value</td>
086 * </tr>
087 * <tr>
088 * <td>Value.WeightWeightedConflicts</td>
089 * <td>{@link Double}</td>
090 * <td>When {@link ConflictStatistics} is used: weighted number of conflicting
091 * variables</td>
092 * </tr>
093 * <tr>
094 * <td>Value.WeightPotentialConflicts</td>
095 * <td>{@link Double}</td>
096 * <td>When {@link ConflictStatistics} is used: weighted number of potentially
097 * conflicting variables</td>
098 * </tr>
099 * <tr>
100 * <td>Value.WeightConflicts</td>
101 * <td>{@link Double}</td>
102 * <td>Number of conflicting variables {@link Model#conflictValues(Value)}.</td>
103 * </tr>
104 * <tr>
105 * <td>Value.WeightNrAssignments</td>
106 * <td>{@link Double}</td>
107 * <td>Number of previous assignments of the value</td>
108 * </tr>
109 * <tr>
110 * <td>Value.WeightValue</td>
111 * <td>{@link Double}</td>
112 * <td>Value {@link Value#toDouble()}</td>
113 * </tr>
114 * </table>
115 * 
116 * @see VariableSelection
117 * @see Solver
118 * 
119 * @version IFS 1.2 (Iterative Forward Search)<br>
120 *          Copyright (C) 2006 - 2010 Tomáš Müller<br>
121 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
122 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
123 * <br>
124 *          This library is free software; you can redistribute it and/or modify
125 *          it under the terms of the GNU Lesser General Public License as
126 *          published by the Free Software Foundation; either version 3 of the
127 *          License, or (at your option) any later version. <br>
128 * <br>
129 *          This library is distributed in the hope that it will be useful, but
130 *          WITHOUT ANY WARRANTY; without even the implied warranty of
131 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
132 *          Lesser General Public License for more details. <br>
133 * <br>
134 *          You should have received a copy of the GNU Lesser General Public
135 *          License along with this library; if not see <http://www.gnu.org/licenses/>.
136 **/
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 iWeightNrAssignments = 0.5;
149    private double iWeightValue = 0.0;
150
151    protected int iTabuSize = 0;
152    protected ArrayList<T> iTabu = null;
153    protected int iTabuPos = 0;
154
155    private boolean iMPP = false;
156    private ConflictStatistics<V, T> iStat = null;
157    private MacPropagation<V, T> iProp = null;
158    private ViolatedInitials<V, T> iViolatedInitials = null;
159
160    public GeneralValueSelection() {
161    }
162
163    /**
164     * Constructor
165     * 
166     * @param properties
167     *            input configuration
168     */
169    public GeneralValueSelection(DataProperties properties) {
170        iMPP = properties.getPropertyBoolean("General.MPP", false);
171        if (iMPP) {
172            iMPPLimit = properties.getPropertyInt("Value.MPPLimit", -1);
173            iInitialSelectionProb = properties.getPropertyDouble("Value.InitialSelectionProb", 0.75);
174            iWeightDeltaInitialAssignment = properties.getPropertyDouble("Value.WeightDeltaInitialAssignments", 0.0);
175        }
176        iGoodSelectionProb = properties.getPropertyDouble("Value.GoodSelectionProb", 0.00);
177        iWeightWeightedCoflicts = properties.getPropertyDouble("Value.WeightWeightedConflicts", 1.0);
178        iWeightPotentialConflicts = properties.getPropertyDouble("Value.WeightPotentialConflicts", 0.0);
179
180        iRandomWalkProb = properties.getPropertyDouble("Value.RandomWalkProb", 0.0);
181        iWeightCoflicts = properties.getPropertyDouble("Value.WeightConflicts", 1.0);
182        iWeightNrAssignments = properties.getPropertyDouble("Value.WeightNrAssignments", 0.5);
183        iWeightValue = properties.getPropertyDouble("Value.WeightValue", 0.0);
184        iTabuSize = properties.getPropertyInt("Value.Tabu", 0);
185        if (iTabuSize > 0)
186            iTabu = new ArrayList<T>(iTabuSize);
187    }
188
189    /** Initialization */
190    @Override
191    public void init(Solver<V, T> solver) {
192        for (Extension<V, T> extension : solver.getExtensions()) {
193            if (ConflictStatistics.class.isInstance(extension))
194                iStat = (ConflictStatistics<V, T>) extension;
195            if (MacPropagation.class.isInstance(extension))
196                iProp = (MacPropagation<V, T>) extension;
197            if (ViolatedInitials.class.isInstance(extension))
198                iViolatedInitials = (ViolatedInitials<V, T>) extension;
199        }
200    }
201
202    /** Value selection */
203    @Override
204    public T selectValue(Solution<V, T> solution, V selectedVariable) {
205        if (iMPP) {
206            if (selectedVariable.getInitialAssignment() != null) {
207                if (solution.getModel().nrUnassignedVariables() == 0) {
208                    if (solution.getModel().perturbVariables().size() <= iMPPLimit)
209                        iMPPLimit = solution.getModel().perturbVariables().size() - 1;
210                }
211                if (iMPPLimit >= 0 && solution.getModel().perturbVariables().size() > iMPPLimit)
212                    return selectedVariable.getInitialAssignment();
213                if (selectedVariable.getInitialAssignment() != null && ToolBox.random() <= iInitialSelectionProb)
214                    return selectedVariable.getInitialAssignment();
215            }
216        }
217
218        List<T> values = selectedVariable.values();
219        if (ToolBox.random() <= iRandomWalkProb)
220            return ToolBox.random(values);
221        if (iProp != null && selectedVariable.getAssignment() == null && ToolBox.random() <= iGoodSelectionProb) {
222            Collection<T> goodValues = iProp.goodValues(selectedVariable);
223            if (!goodValues.isEmpty())
224                values = new ArrayList<T>(goodValues);
225        }
226        if (values.size() == 1)
227            return values.get(0);
228
229        List<T> bestValues = null;
230        double bestWeightedSum = 0;
231
232        for (T value : values) {
233            if (iTabu != null && iTabu.contains(value))
234                continue;
235            if (selectedVariable.getAssignment() != null && selectedVariable.getAssignment().equals(value))
236                continue;
237
238            Collection<T> conf = solution.getModel().conflictValues(value);
239            if (conf.contains(value))
240                continue;
241
242            double weightedConflicts = (iStat == null || iWeightWeightedCoflicts == 0.0 ? 0.0 : iStat.countRemovals(
243                    solution.getIteration(), conf, value));
244            double potentialConflicts = (iStat == null || iWeightPotentialConflicts == 0.0 ? 0.0 : iStat
245                    .countPotentialConflicts(solution.getIteration(), value, 3));
246
247            long deltaInitialAssignments = 0;
248            if (iMPP && iWeightDeltaInitialAssignment != 0.0) {
249                if (iViolatedInitials != null) {
250                    Set<T> violations = iViolatedInitials.getViolatedInitials(value);
251                    if (violations != null) {
252                        for (T aValue : violations) {
253                            if (aValue.variable().getAssignment() == null
254                                    || aValue.variable().getAssignment().equals(aValue))
255                                deltaInitialAssignments += 2;
256                        }
257                    }
258                }
259                for (Iterator<T> it1 = conf.iterator(); it1.hasNext();) {
260                    T aValue = it1.next();
261                    if (aValue.variable().getInitialAssignment() != null)
262                        deltaInitialAssignments--;
263                }
264                if (selectedVariable.getInitialAssignment() != null
265                        && !selectedVariable.getInitialAssignment().equals(value)) {
266                    deltaInitialAssignments++;
267                }
268                if (iMPPLimit >= 0
269                        && (solution.getModel().perturbVariables().size() + deltaInitialAssignments) > iMPPLimit)
270                    continue;
271            }
272
273            double weightedSum = (iWeightDeltaInitialAssignment * deltaInitialAssignments)
274                    + (iWeightPotentialConflicts * potentialConflicts) + (iWeightWeightedCoflicts * weightedConflicts)
275                    + (iWeightCoflicts * conf.size()) + (iWeightNrAssignments * value.countAssignments())
276                    + (iWeightValue * value.toDouble());
277
278            if (bestValues == null || bestWeightedSum > weightedSum) {
279                bestWeightedSum = weightedSum;
280                if (bestValues == null)
281                    bestValues = new ArrayList<T>();
282                else
283                    bestValues.clear();
284                bestValues.add(value);
285            } else {
286                if (bestWeightedSum == weightedSum)
287                    bestValues.add(value);
288            }
289        }
290
291        T selectedValue = (bestValues == null ? null : ToolBox.random(bestValues));
292        if (selectedValue == null)
293            selectedValue = ToolBox.random(values);
294        if (iTabu != null) {
295            if (iTabu.size() == iTabuPos)
296                iTabu.add(selectedValue);
297            else
298                iTabu.set(iTabuPos, selectedValue);
299            iTabuPos = (iTabuPos + 1) % iTabuSize;
300        }
301        return (bestValues == null ? null : selectedValue);
302    }
303
304}