1 | /* -*- C++ -*- |
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2 | * |
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3 | * This file is a part of LEMON, a generic C++ optimization library |
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4 | * |
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5 | * Copyright (C) 2003-2006 |
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6 | * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport |
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7 | * (Egervary Research Group on Combinatorial Optimization, EGRES). |
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8 | * |
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9 | * Permission to use, modify and distribute this software is granted |
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10 | * provided that this copyright notice appears in all copies. For |
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11 | * precise terms see the accompanying LICENSE file. |
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12 | * |
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13 | * This software is provided "AS IS" with no warranty of any kind, |
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14 | * express or implied, and with no claim as to its suitability for any |
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15 | * purpose. |
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16 | * |
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17 | */ |
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18 | |
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19 | |
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20 | #ifndef LEMON_TABU_SEARCH_H |
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21 | #define LEMON_TABU_SEARCH_H |
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22 | |
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23 | /// \ingroup experimental |
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24 | /// \file |
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25 | /// \brief TabuSearch algorithm. |
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26 | /// |
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27 | /// \author Szabadkai Mark |
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28 | |
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29 | #include <lemon/bits/utility.h> |
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30 | #include <lemon/error.h> |
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31 | #include <lemon/time_measure.h> |
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32 | #include <functional> |
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33 | #include <deque> |
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34 | |
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35 | |
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36 | namespace lemon { |
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37 | |
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38 | /// \brief Default Traits for TabuSearch class. |
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39 | /// |
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40 | /// This template defines the needed types for the \ref TabuSearch class. |
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41 | /// Is main purpos is to simplify the main class's template interface, |
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42 | /// but it provides the EdgeIt type, passing to the concrete graph wheter |
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43 | /// it is directed or undirected. |
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44 | #ifdef DOXYGEN |
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45 | template< typename GRAPH, typename VALUE, |
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46 | typename HEIGHTMAP, typename BETTER, bool UNDIR > |
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47 | #else |
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48 | template< typename GRAPH, typename VALUE, |
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49 | typename HEIGHTMAP = typename GRAPH::template NodeMap<VALUE>, |
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50 | typename BETTER = std::less<VALUE>, |
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51 | bool UNDIR = UndirectedTagIndicator<GRAPH>::value > |
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52 | #endif |
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53 | struct TabuSearchDefaultTraits { |
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54 | typedef VALUE Value; |
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55 | typedef BETTER Better; |
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56 | |
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57 | typedef GRAPH Graph; |
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58 | typedef typename GRAPH::Node Node; |
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59 | typedef HEIGHTMAP HeightMap; |
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60 | |
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61 | typedef typename GRAPH::IncEdgeIt EdgeIt; |
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62 | }; |
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63 | |
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64 | template< typename GRAPH, typename VALUE, |
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65 | typename HEIGHTMAP, typename BETTER > |
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66 | struct TabuSearchDefaultTraits< GRAPH, VALUE, HEIGHTMAP, BETTER, false > { |
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67 | typedef VALUE Value; |
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68 | typedef BETTER Better; |
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69 | |
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70 | typedef GRAPH Graph; |
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71 | typedef typename GRAPH::Node Node; |
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72 | typedef HEIGHTMAP HeightMap; |
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73 | |
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74 | typedef typename GRAPH::OutEdgeIt EdgeIt; |
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75 | }; |
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76 | |
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77 | |
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78 | |
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79 | /// \brief Policy hierarchy to controll the search algorithm. |
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80 | /// |
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81 | /// The fallowing template hierarchy offers a clean interface to define own |
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82 | /// policies, and combine existing ones. |
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83 | template< typename TS > |
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84 | struct TabuSearchPolicyConcept { |
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85 | void target( TS *ts ) {} |
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86 | |
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87 | void reset() {} |
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88 | bool onStep() { return false; } |
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89 | bool onStick() { return false; } |
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90 | bool onImprove( const typename TS::Value &best ) { return false; } |
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91 | }; |
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92 | |
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93 | template< typename TS > |
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94 | struct YesPolicy { |
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95 | void target( TS *ts ) {} |
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96 | |
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97 | void reset() {} |
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98 | bool onStep() { return true; } |
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99 | bool onStick() { return true; } |
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100 | bool onImprove( const typename TS::Value &best ) { return true; } |
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101 | }; |
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102 | |
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103 | template< typename TS > |
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104 | struct NoPolicy : public TabuSearchPolicyConcept<TS> {}; |
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105 | |
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106 | /// \brief Some basic methode, how tow Policies can be combined |
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107 | struct PolicyAndCombination { |
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108 | static bool evaluate( const bool r1, const bool r2 ) { |
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109 | return r1 && r2; |
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110 | } |
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111 | }; |
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112 | |
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113 | struct PolicyOrCombination { |
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114 | static bool evaluate( const bool r1, const bool r2 ) { |
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115 | return r1 || r2; |
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116 | } |
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117 | }; |
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118 | |
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119 | /// \brief CombinePolicies |
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120 | /// |
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121 | /// It combines tow policies using the given combination methode (mainly |
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122 | /// some of the basic logical methodes) to create a new one. |
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123 | #ifdef DOXYGEN |
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124 | template< template<typename> class CP1, template<typename> class CP2, |
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125 | typename COMBINATION > |
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126 | #else |
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127 | template< template<typename> class CP1, template<typename> class CP2, |
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128 | typename COMBINATION = PolicyAndCombination > |
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129 | #endif |
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130 | struct CombinePolicies { |
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131 | template< typename TS > |
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132 | struct Policy { |
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133 | typedef CP1<TS> Policy1; |
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134 | typedef CP2<TS> Policy2; |
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135 | |
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136 | Policy1 policy1; |
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137 | Policy2 policy2; |
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138 | |
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139 | inline Policy() : policy1(), policy2() {} |
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140 | inline Policy( const Policy1 &cp1, const Policy2 &cp2 ) |
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141 | : policy1(cp1), policy2(cp2) {} |
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142 | |
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143 | void target( TS *ts ) { |
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144 | policy1.target(ts), policy2.target(ts); |
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145 | }; |
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146 | |
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147 | void reset() { |
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148 | policy1.reset(), policy2.reset(); |
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149 | } |
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150 | |
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151 | bool onStep() { |
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152 | return cmb.evaluate( policy1.onStep(), policy2.onStep() ); |
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153 | } |
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154 | |
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155 | bool onStick() { |
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156 | return cmb.evaluate( policy1.onStick(), policy2.onStick() ); |
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157 | } |
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158 | |
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159 | bool onImprove( const typename TS::Value &best ) { |
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160 | return cmb.evaluate( policy1.onImprove(best), |
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161 | policy2.onImprove(best) ); |
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162 | } |
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163 | |
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164 | private: |
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165 | COMBINATION cmb; |
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166 | }; |
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167 | }; |
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168 | |
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169 | |
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170 | /// \brief IterationPolicy limits the number of iterations and the |
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171 | /// number of iterations without improvement |
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172 | template< typename TS > |
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173 | struct IterationPolicy { |
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174 | IterationPolicy() : _it_lim(100000), _noimpr_it_lim(5000) {} |
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175 | IterationPolicy( const long int itl, const long int noimpritl ) |
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176 | : _it_lim(itl), _noimpr_it_lim(noimpritl) |
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177 | {} |
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178 | |
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179 | void target( TS *ts ) {} |
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180 | |
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181 | void reset() { |
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182 | _it = _noimpr_it = 0; |
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183 | } |
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184 | |
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185 | bool onStep() { |
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186 | ++_it; ++_noimpr_it; |
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187 | return (_it <= _it_lim) && (_noimpr_it <= _noimpr_it_lim); |
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188 | } |
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189 | |
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190 | bool onStick() { |
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191 | return false; |
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192 | } |
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193 | |
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194 | bool onImprove( const typename TS::Value &best ) { |
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195 | _noimpr_it = 0; |
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196 | return true; |
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197 | } |
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198 | |
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199 | long int iterationLimit() const { |
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200 | return _it_lim; |
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201 | } |
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202 | |
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203 | void iterationLimit( const long int itl ) { |
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204 | _it_lim = itl; |
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205 | } |
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206 | |
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207 | long int noImprovementIterationLimit() const { |
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208 | return _noimpr_it_lim; |
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209 | } |
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210 | |
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211 | void noImprovementIterationLimit( const long int noimpritl ) { |
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212 | _noimpr_it_lim = noimpritl; |
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213 | } |
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214 | |
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215 | private: |
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216 | long int _it_lim, _noimpr_it_lim; |
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217 | long int _it, _noimpr_it; |
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218 | }; |
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219 | |
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220 | /// \brief HeightPolicy stops the search when a given height is reached or |
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221 | /// exceeds |
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222 | template< typename TS > |
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223 | struct HeightPolicy { |
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224 | typedef typename TS::Value Value; |
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225 | |
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226 | HeightPolicy() : _height_lim(), _found(false) {} |
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227 | HeightPolicy( const Value &hl ) : _height_lim(hl), _found(false) {} |
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228 | |
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229 | void target( TS *ts ) {} |
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230 | |
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231 | void reset() { |
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232 | _found = false; |
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233 | } |
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234 | |
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235 | bool onStep() { |
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236 | return !_found; |
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237 | } |
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238 | |
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239 | bool onStick() { |
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240 | return false; |
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241 | } |
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242 | |
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243 | bool onImprove( const Value &best ) { |
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244 | typename TS::Better better; |
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245 | _found = better(best, _height_lim) || (best == _height_lim); |
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246 | return !_found; |
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247 | } |
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248 | |
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249 | Value heightLimi() const { |
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250 | return _height_lim; |
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251 | } |
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252 | |
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253 | void heightLimi( const Value &hl ) { |
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254 | _height_lim = hl; |
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255 | } |
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256 | |
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257 | private: |
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258 | Value _height_lim; |
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259 | bool _found; |
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260 | }; |
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261 | |
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262 | /// \brief TimePolicy limits the time for searching. |
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263 | template< typename TS > |
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264 | struct TimePolicy { |
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265 | TimePolicy() : _time_lim(60.0), _timeisup(false) {} |
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266 | TimePolicy( const double tl ) : _time_lim(tl), _timeisup(false) {} |
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267 | |
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268 | void target( TS *ts ) {} |
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269 | |
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270 | void reset() { |
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271 | _timeisup = false; |
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272 | _t.reset(); |
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273 | } |
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274 | |
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275 | bool onStep() { |
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276 | update(); |
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277 | return !_timeisup; |
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278 | } |
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279 | |
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280 | bool onStick() { |
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281 | return false; |
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282 | } |
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283 | |
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284 | bool onImprove( const typename TS::Value &best ) { |
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285 | update(); |
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286 | return !_timeisup; |
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287 | } |
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288 | |
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289 | double timeLimit() const { |
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290 | return _time_lim; |
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291 | } |
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292 | |
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293 | void setTimeLimit( const double tl ) { |
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294 | _time_lim = tl; |
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295 | update(); |
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296 | } |
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297 | |
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298 | private: |
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299 | lemon::Timer _t; |
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300 | double _time_lim; |
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301 | bool _timeisup; |
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302 | |
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303 | inline void update() { |
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304 | _timeisup = _t.realTime() > _time_lim; |
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305 | } |
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306 | }; |
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307 | |
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308 | |
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309 | |
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310 | /// \brief TabuSearch main class |
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311 | /// |
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312 | /// This class offers the implementation of tabu-search algorithm. The |
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313 | /// tabu-serach is a local-search. It starts from a specified point of the |
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314 | /// problem's graph representation, and in every step it goes to the localy |
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315 | /// best next Node except those in tabu set. The maximum size of this tabu |
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316 | /// set defines how many Node will be remembered. The best Node ever found |
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317 | /// will also stored, so we wont lose it, even is the search continues. |
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318 | /// The class can be used on any kind of Graph and with any kind of Value |
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319 | /// with a total-settlement on it. |
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320 | /// |
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321 | /// \param _Graph The graph type the algorithm runs on. |
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322 | /// \param _Value The values' type associated to the nodes. |
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323 | /// \param _Policy Controlls the search. Determinates when to stop, or how |
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324 | /// manage stuck search. Default value is \ref IterationPolicy . |
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325 | /// \param _Traits Collection of needed types. Default value is |
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326 | /// \ref TabuSearchDefaultTraits . |
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327 | /// |
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328 | /// \author Szabadkai Mark |
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329 | #ifdef DOXYGEN |
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330 | template< typename GRAPH, typename VALUE, template<typename> class POLICY, typename TRAITS > |
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331 | #else |
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332 | template< typename GRAPH, typename VALUE, |
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333 | template<typename> class POLICY = IterationPolicy, |
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334 | typename TRAITS = TabuSearchDefaultTraits<GRAPH, VALUE> > |
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335 | #endif |
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336 | class TabuSearch |
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337 | { |
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338 | public: |
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339 | |
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340 | /// \brief Thrown by setting the size of the tabu-set and the given size |
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341 | /// is less than 2. |
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342 | class BadParameterError : public lemon::LogicError { |
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343 | public: |
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344 | virtual const char* exceptionName() const { |
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345 | return "lemon::TabuSearch::BadParameterError"; |
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346 | } |
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347 | }; |
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348 | |
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349 | ///Public types |
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350 | typedef TabuSearch<GRAPH,VALUE,POLICY,TRAITS> SelfType; |
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351 | |
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352 | typedef typename TRAITS::Graph Graph; |
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353 | typedef typename TRAITS::Node Node; |
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354 | typedef typename TRAITS::Value Value; |
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355 | typedef typename TRAITS::HeightMap HeightMap; |
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356 | typedef typename TRAITS::Better Better; |
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357 | typedef typename std::deque< Node >::const_iterator TabuIterator; |
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358 | |
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359 | typedef POLICY<SelfType> Policy; |
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360 | |
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361 | protected: |
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362 | typedef typename TRAITS::EdgeIt EdgeIt; |
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363 | |
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364 | const Graph &gr; |
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365 | const HeightMap &height; |
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366 | /// The tabu set. Teh current node is the first |
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367 | std::deque< Node > tabu; |
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368 | /// Maximal tabu size |
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369 | unsigned int mts; |
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370 | /// The best Node found |
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371 | Node b; |
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372 | |
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373 | Better better; |
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374 | Policy pol; |
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375 | |
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376 | public: |
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377 | /// \brief Constructor |
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378 | /// |
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379 | /// \param graph the graph the algorithm will run on. |
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380 | /// \param hm the height map used by the algorithm. |
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381 | /// \param tabusz the maximal size of the tabu set. Default value is 3 |
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382 | /// \param p the Policy controlling the search. |
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383 | TabuSearch( const Graph &graph, const HeightMap &hm, |
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384 | const int tabusz = 3, Policy p = Policy() ) |
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385 | : gr(graph), height(hm), mts(tabusz), pol(p) |
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386 | { |
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387 | pol.target(this); |
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388 | } |
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389 | |
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390 | /// \brief Destructor |
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391 | ~TabuSearch() { |
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392 | pol.target(NULL); |
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393 | } |
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394 | |
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395 | /// Set/Get the size of the tabu set |
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396 | void tabuSize( const unsigned int size ) |
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397 | { |
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398 | if( size < 2 ) |
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399 | throw BadParameterError( "Tabu size must be at least 2!" ); |
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400 | mts = size; |
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401 | while( mts < tabu.size() ) |
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402 | tabu.pop_back(); |
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403 | } |
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404 | |
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405 | unsigned int tabuSize() const { |
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406 | return mts; |
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407 | } |
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408 | |
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409 | /// Set/Get Policy |
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410 | void policy( Policy p ) { |
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411 | pol.target(NULL); |
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412 | pol = p; |
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413 | pol.target(this); |
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414 | } |
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415 | |
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416 | Policy& policy() { |
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417 | return pol; |
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418 | } |
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419 | |
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420 | /// \name Execution control |
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421 | /// The simplest way to execute the algorithm is to use the member |
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422 | /// functions called \c run( 'startnode' ). |
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423 | ///@{ |
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424 | |
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425 | /// \brief Initializes the internal data. |
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426 | /// |
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427 | /// \param startn The start node where the search begins. |
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428 | void init( const Node startn ) { |
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429 | tabu.clear(); |
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430 | tabu.push_front( startn ); |
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431 | b = startn; |
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432 | pol.reset(); |
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433 | } |
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434 | |
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435 | /// \brief Does one iteration |
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436 | /// |
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437 | /// If the Policy allows it searches for the best next node, then steps |
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438 | /// onto it. |
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439 | /// \return %False if one Policy condition wants to stop the search. |
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440 | bool step() |
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441 | { |
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442 | ///Request premmision from ControllPolicy |
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443 | if( !pol.onStep() ) |
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444 | return false; |
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445 | |
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446 | ///Find the best next potential node |
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447 | Node n; bool found = false; |
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448 | for( EdgeIt e(gr,tabu[0]); e != INVALID; ++e ) |
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449 | { |
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450 | Node m = (gr.source(e) == tabu[0]) ? gr.target(e) : gr.source(e); |
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451 | bool wrong = false; |
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452 | for( int i = 1; i != (signed int)tabu.size(); ++i ) |
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453 | if( m == tabu[i] ) { |
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454 | wrong = true; |
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455 | break; |
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456 | } |
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457 | if( wrong ) |
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458 | continue; |
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459 | |
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460 | if( !found ) { |
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461 | n = m; |
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462 | found = true; |
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463 | } else |
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464 | if( better(height[m], height[n]) ) { |
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465 | n = m; |
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466 | } |
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467 | } |
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468 | |
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469 | ///Handle stuck search |
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470 | if( !found ) { |
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471 | return pol.onStick(); |
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472 | } |
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473 | |
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474 | ///Move on... |
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475 | tabu.push_front(n); |
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476 | while( mts < tabu.size() ) { |
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477 | tabu.pop_back(); |
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478 | } |
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479 | if( better(height[n], height[b]) ) { |
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480 | b = n; |
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481 | if( !pol.onImprove(height[b]) ) |
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482 | return false; |
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483 | } |
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484 | |
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485 | return true; |
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486 | } |
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487 | |
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488 | /// \brief Runs a search while the Policy stops it. |
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489 | /// |
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490 | /// \param startn The start node where the search begins. |
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491 | inline void run( const Node startn ) { |
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492 | std::cin.unsetf( std::ios_base::skipws ); |
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493 | char c; |
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494 | init( startn ); |
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495 | while( step() ) |
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496 | std::cin >> c; |
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497 | std::cin.setf( std::ios_base::skipws ); |
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498 | } |
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499 | |
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500 | ///@} |
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501 | |
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502 | /// \name Query Functions |
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503 | /// The result of the TabuSearch algorithm can be obtained using these |
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504 | /// functions.\n |
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505 | ///@{ |
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506 | |
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507 | /// \brief The node, the search is standing on. |
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508 | inline Node current() const { |
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509 | return tabu[0]; |
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510 | } |
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511 | |
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512 | /// \brief The best node found until now. |
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513 | inline Node best() const { |
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514 | return b; |
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515 | } |
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516 | |
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517 | /// \brief Beginning to iterate on the current tabu set. |
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518 | inline TabuIterator tabu_begin() const { |
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519 | return tabu.begin(); |
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520 | } |
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521 | |
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522 | /// \brief Ending to iterate on the current tabu set. |
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523 | inline TabuIterator tabu_end() const { |
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524 | return tabu.end(); |
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525 | } |
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526 | |
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527 | ///@} |
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528 | }; |
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529 | } |
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530 | #endif |
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