    001package org.cpsolver.exam.heuristics;
    002
    003import java.util.ArrayList;
    004import java.util.Collection;
    005import java.util.List;
    006import java.util.Map;
    007
    008import org.apache.log4j.Logger;
    009import org.cpsolver.exam.model.Exam;
    010import org.cpsolver.exam.model.ExamPlacement;
    011import org.cpsolver.exam.neighbours.ExamRandomMove;
    012import org.cpsolver.exam.neighbours.ExamRoomMove;
    013import org.cpsolver.exam.neighbours.ExamSimpleNeighbour;
    014import org.cpsolver.exam.neighbours.ExamTimeMove;
    015import org.cpsolver.ifs.assignment.Assignment;
    016import org.cpsolver.ifs.assignment.context.AssignmentContext;
    017import org.cpsolver.ifs.assignment.context.NeighbourSelectionWithContext;
    018import org.cpsolver.ifs.heuristics.NeighbourSelection;
    019import org.cpsolver.ifs.model.LazyNeighbour;
    020import org.cpsolver.ifs.model.Model;
    021import org.cpsolver.ifs.model.Neighbour;
    022import org.cpsolver.ifs.model.LazyNeighbour.LazyNeighbourAcceptanceCriterion;
    023import org.cpsolver.ifs.solution.Solution;
    024import org.cpsolver.ifs.solution.SolutionListener;
    025import org.cpsolver.ifs.solver.Solver;
    026import org.cpsolver.ifs.util.DataProperties;
    027import org.cpsolver.ifs.util.Progress;
    028import org.cpsolver.ifs.util.ToolBox;
    029
    030
    031/**
    032 * Hill climber. In each iteration, one of the following three neighbourhoods is
    033 * selected first
    034 * <ul>
    035 * <li>random move ({@link ExamRandomMove})
    036 * <li>period swap ({@link ExamTimeMove})
    037 * <li>room swap ({@link ExamRoomMove})
    038 * </ul>
    039 * , then a neighbour is generated and it is accepted if its value
    040 * {@link ExamSimpleNeighbour#value(Assignment)} is below or equal to zero. The search is
    041 * stopped after a given amount of idle iterations ( can be defined by problem
    042 * property HillClimber.MaxIdle). <br>
    043 * <br>
    044 *
    045 * @version ExamTT 1.3 (Examination Timetabling)<br>
    046 *          Copyright (C) 2008 - 2014 Tomáš Müller<br>
    047 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
    048 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
    049 * <br>
    050 *          This library is free software; you can redistribute it and/or modify
    051 *          it under the terms of the GNU Lesser General Public License as
    052 *          published by the Free Software Foundation; either version 3 of the
    053 *          License, or (at your option) any later version. <br>
    054 * <br>
    055 *          This library is distributed in the hope that it will be useful, but
    056 *          WITHOUT ANY WARRANTY; without even the implied warranty of
    057 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
    058 *          Lesser General Public License for more details. <br>
    059 * <br>
    060 *          You should have received a copy of the GNU Lesser General Public
    061 *          License along with this library; if not see
    062 *          <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>.
    063 */
    064@Deprecated
    065public class ExamHillClimbing extends NeighbourSelectionWithContext<Exam, ExamPlacement, ExamHillClimbing.Context> implements SolutionListener<Exam, ExamPlacement>, LazyNeighbourAcceptanceCriterion<Exam, ExamPlacement> {
    066    private static Logger sLog = Logger.getLogger(ExamHillClimbing.class);
    067    private List<NeighbourSelection<Exam, ExamPlacement>> iNeighbours = null;
    068    private int iMaxIdleIters = 25000;
    069    private Progress iProgress = null;
    070    private String iName = "Hill climbing";
    071
    072    /**
    073     * Constructor
    074     *
    075     * @param properties
    076     *            problem properties (use HillClimber.MaxIdle to set maximum
    077     *            number of idle iterations)
    078     */
    079    public ExamHillClimbing(DataProperties properties) {
    080        this(properties, "Hill Climbing");
    081    }
    082
    083    /**
    084     * Constructor
    085     *
    086     * @param properties
    087     *            problem properties (use HillClimber.MaxIdle to set maximum
    088     *            number of idle iterations)
    089     * @param name solver search phase name
    090     */
    091    @SuppressWarnings("unchecked")
    092    public ExamHillClimbing(DataProperties properties, String name) {
    093        iMaxIdleIters = properties.getPropertyInt("HillClimber.MaxIdle", iMaxIdleIters);
    094        String neighbours = properties.getProperty("HillClimber.Neighbours",
    095                ExamRandomMove.class.getName() + ";" +
    096                ExamRoomMove.class.getName() + ";" +
    097                ExamTimeMove.class.getName());
    098        neighbours += ";" + properties.getProperty("HillClimber.AdditionalNeighbours", "");
    099        iNeighbours = new ArrayList<NeighbourSelection<Exam,ExamPlacement>>();
    100        for (String neighbour: neighbours.split("\\;")) {
    101            if (neighbour == null || neighbour.isEmpty()) continue;
    102            try {
    103                Class<NeighbourSelection<Exam, ExamPlacement>> clazz = (Class<NeighbourSelection<Exam, ExamPlacement>>)Class.forName(neighbour);
    104                iNeighbours.add(clazz.getConstructor(DataProperties.class).newInstance(properties));
    105            } catch (Exception e) {
    106                sLog.error("Unable to use " + neighbour + ": " + e.getMessage());
    107            }
    108        }
    109        iName = name;
    110    }
    111
    112    /**
    113     * Initialization
    114     */
    115    @Override
    116    public void init(Solver<Exam, ExamPlacement> solver) {
    117        super.init(solver);
    118        solver.currentSolution().addSolutionListener(this);
    119        for (NeighbourSelection<Exam, ExamPlacement> neighbour: iNeighbours)
    120            neighbour.init(solver);
    121        solver.setUpdateProgress(false);
    122        iProgress = Progress.getInstance(solver.currentSolution().getModel());
    123        getContext(solver.currentSolution().getAssignment()).reset();
    124    }
    125
    126    /**
    127     * Select one of the given neighbourhoods randomly, select neighbour, return
    128     * it if its value is below or equal to zero (continue with the next
    129     * selection otherwise). Return null when the given number of idle
    130     * iterations is reached.
    131     */
    132    @Override
    133    public Neighbour<Exam, ExamPlacement> selectNeighbour(Solution<Exam, ExamPlacement> solution) {
    134        Context context = getContext(solution.getAssignment());
    135        context.activateIfNeeded();
    136        while (true) {
    137            if (context.incIter(solution)) break;
    138            NeighbourSelection<Exam, ExamPlacement> ns = iNeighbours.get(ToolBox.random(iNeighbours.size()));
    139            Neighbour<Exam, ExamPlacement> n = ns.selectNeighbour(solution);
    140            if (n != null) {
    141                if (n instanceof LazyNeighbour) {
    142                    ((LazyNeighbour<Exam,ExamPlacement>)n).setAcceptanceCriterion(this);
    143                    return n;
    144                } else if (n.value(solution.getAssignment()) <= 0.0) return n;
    145            }
    146        }
    147        context.reset();
    148        return null;
    149    }
    150
    151    /**
    152     * Memorize the iteration when the last best solution was found.
    153     */
    154    @Override
    155    public void bestSaved(Solution<Exam, ExamPlacement> solution) {
    156        getContext(solution.getAssignment()).bestSaved(solution.getModel());
    157    }
    158
    159    @Override
    160    public void solutionUpdated(Solution<Exam, ExamPlacement> solution) {
    161    }
    162
    163    @Override
    164    public void getInfo(Solution<Exam, ExamPlacement> solution, Map<String, String> info) {
    165    }
    166
    167    @Override
    168    public void getInfo(Solution<Exam, ExamPlacement> solution, Map<String, String> info, Collection<Exam> variables) {
    169    }
    170
    171    @Override
    172    public void bestCleared(Solution<Exam, ExamPlacement> solution) {
    173    }
    174
    175    @Override
    176    public void bestRestored(Solution<Exam, ExamPlacement> solution) {
    177    }
    178
    179    /** Accept lazy neighbour */
    180    @Override
    181    public boolean accept(Assignment<Exam, ExamPlacement> assignment, LazyNeighbour<Exam, ExamPlacement> neighbour, double value) {
    182        return value <= 0.0;
    183    }
    184
    185    @Override
    186    public Context createAssignmentContext(Assignment<Exam, ExamPlacement> assignment) {
    187        return new Context();
    188    }
    189
    190    public class Context implements AssignmentContext {
    191        private int iLastImprovingIter = 0;
    192        private double iBestValue = 0;
    193        private int iIter = 0;
    194        private boolean iActive;
    195
    196        protected void reset() {
    197            iIter = 0;
    198            iLastImprovingIter = 0;
    199            iActive = false;
    200        }
    201
    202        protected void activateIfNeeded() {
    203            if (!iActive) {
    204                iProgress.setPhase(iName + "...");
    205                iActive = true;
    206            }
    207        }
    208
    209        protected boolean incIter(Solution<Exam, ExamPlacement> solution) {
    210            iIter++;
    211            iProgress.setProgress(Math.round(100.0 * (iIter - iLastImprovingIter) / iMaxIdleIters));
    212            if (iIter - iLastImprovingIter >= iMaxIdleIters) return true;
    213            return false;
    214        }
    215
    216        protected void bestSaved(Model<Exam, ExamPlacement> model) {
    217            if (Math.abs(iBestValue - model.getBestValue()) >= 1.0) {
    218                iLastImprovingIter = iIter;
    219                iBestValue = model.getBestValue();
    220            }
    221        }
    222    }
    223}