1 | #ifndef LEMON_SIMANN_H |
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2 | #define LEMON_SIMANN_H |
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3 | |
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4 | /// \ingroup experimental |
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5 | /// \file |
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6 | /// \brief Simulated annealing framework. |
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7 | /// \author Akos Ladanyi |
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8 | |
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9 | #include <cstdlib> |
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10 | #include <cmath> |
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11 | #include <lemon/time_measure.h> |
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12 | |
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13 | namespace lemon { |
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14 | |
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15 | /// \addtogroup experimental |
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16 | /// @{ |
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17 | |
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18 | const double INFTY = 1e24; |
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19 | |
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20 | /*! \brief Simulated annealing base class. */ |
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21 | class SimAnnBase { |
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22 | public: |
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23 | class Controller; |
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24 | private: |
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25 | /*! Pointer to the controller. */ |
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26 | Controller *controller; |
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27 | protected: |
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28 | /*! \brief Cost of the current solution. */ |
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29 | double curr_cost; |
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30 | /*! \brief Cost of the best solution. */ |
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31 | double best_cost; |
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32 | /*! \brief Cost of the previous solution. */ |
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33 | double prev_cost; |
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34 | /*! \brief Cost of the solution preceding the previous one. */ |
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35 | double prev_prev_cost; |
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36 | |
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37 | /*! \brief Step to a neighbouring state. */ |
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38 | virtual void mutate() {} |
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39 | /*! \brief Reverts the last mutate(). */ |
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40 | virtual void revert() {} |
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41 | /*! \brief Saves the current solution as the best one. */ |
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42 | virtual void saveAsBest() {} |
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43 | public: |
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44 | /*! \brief Constructor. */ |
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45 | SimAnnBase() { |
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46 | best_cost = prev_cost = prev_prev_cost = INFTY; |
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47 | } |
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48 | /*! \brief Sets the controller class to use. */ |
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49 | void setController(Controller &_controller) { |
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50 | controller = &_controller; |
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51 | controller->setBase(this); |
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52 | } |
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53 | /*! \brief Returns the cost of the current solution. */ |
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54 | double getCurrCost() const { return curr_cost; } |
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55 | /*! \brief Returns the cost of the previous solution. */ |
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56 | double getPrevCost() const { return prev_cost; } |
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57 | /*! \brief Returns the cost of the best solution. */ |
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58 | double getBestCost() const { return best_cost; } |
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59 | /*! \brief Starts the annealing process. */ |
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60 | void run() { |
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61 | controller->init(); |
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62 | do { |
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63 | mutate(); |
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64 | if (controller->accept()) { |
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65 | controller->acceptEvent(); |
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66 | if (curr_cost < best_cost) { |
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67 | saveAsBest(); |
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68 | controller->improveEvent(); |
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69 | } |
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70 | } |
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71 | else { |
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72 | revert(); |
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73 | controller->rejectEvent(); |
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74 | } |
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75 | } while (controller->next()); |
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76 | } |
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77 | |
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78 | /*! \brief A base class for controllers. */ |
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79 | class Controller { |
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80 | public: |
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81 | /*! \brief Pointer to the simulated annealing base class. */ |
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82 | SimAnnBase *base; |
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83 | /*! \brief Initializes the controller. */ |
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84 | virtual void init() {} |
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85 | /*! \brief This is called when a neighbouring state gets accepted. */ |
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86 | virtual void acceptEvent() {} |
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87 | /*! \brief This is called when the accepted neighbouring state's cost is |
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88 | * less than the best found one's. |
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89 | */ |
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90 | virtual void improveEvent() {} |
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91 | /*! \brief This is called when a neighbouring state gets rejected. */ |
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92 | virtual void rejectEvent() {} |
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93 | /*! \brief Sets the simulated annealing base class to use. */ |
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94 | virtual void setBase(SimAnnBase *_base) { base = _base; } |
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95 | /*! \brief Decides whether to continue the annealing process or not. */ |
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96 | virtual bool next() = 0; |
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97 | /*! \brief Decides whether to accept the current solution or not. */ |
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98 | virtual bool accept() = 0; |
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99 | }; |
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100 | }; |
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101 | |
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102 | /*! \brief Simulated annealing class. */ |
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103 | template <typename E> |
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104 | class SimAnn : public SimAnnBase { |
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105 | private: |
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106 | /*! \brief Pointer to the current entity. */ |
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107 | E *curr_ent; |
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108 | /*! \brief Pointer to the best entity. */ |
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109 | E *best_ent; |
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110 | public: |
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111 | /*! \brief Constructor. */ |
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112 | SimAnn() : SimAnnBase() {} |
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113 | /*! \brief Sets the initial entity. */ |
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114 | void setEntity(E &ent) { |
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115 | curr_ent = new E(ent); |
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116 | best_ent = new E(ent); |
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117 | curr_cost = curr_ent->getCost(); |
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118 | } |
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119 | /*! \brief Returns the best found entity. */ |
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120 | E getBestEntity() { return *best_ent; } |
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121 | /*! \brief Step to a neighbouring state. */ |
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122 | void mutate() { |
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123 | prev_prev_cost = prev_cost; |
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124 | prev_cost = curr_cost; |
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125 | curr_ent->mutate(); |
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126 | curr_cost = curr_ent->getCost(); |
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127 | } |
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128 | /*! \brief Reverts the last mutate(). */ |
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129 | void revert() { |
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130 | curr_ent->revert(); |
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131 | curr_cost = prev_cost; |
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132 | prev_cost = prev_prev_cost; |
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133 | } |
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134 | /*! \brief Saves the current solution as the best one. */ |
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135 | void saveAsBest() { |
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136 | delete(best_ent); |
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137 | best_ent = new E(*curr_ent); |
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138 | best_cost = curr_cost; |
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139 | } |
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140 | }; |
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141 | |
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142 | /*! \brief Skeleton of an entity class. */ |
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143 | class EntitySkeleton { |
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144 | public: |
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145 | /*! \brief Returns the cost of the entity. */ |
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146 | double getCost() { return 0.0; } |
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147 | /*! \brief Makes a minor change to the entity. */ |
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148 | void mutate() {} |
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149 | /*! \brief Restores the entity to its previous state i.e. reverts the |
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150 | * effects of the last mutate(). |
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151 | */ |
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152 | void revert() {} |
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153 | }; |
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154 | |
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155 | /*! \brief A simple controller for the simulated annealing class. */ |
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156 | class SimpleController : public SimAnnBase::Controller { |
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157 | public: |
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158 | /*! \brief Number of iterations. */ |
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159 | long iter; |
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160 | /*! \brief Number of iterations which did not improve the solution since |
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161 | * the last improvement. */ |
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162 | long last_impr; |
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163 | /*! \brief Maximum number of iterations. */ |
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164 | long max_iter; |
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165 | /*! \brief Maximum number of iterations which do not improve the |
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166 | * solution. */ |
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167 | long max_no_impr; |
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168 | /*! \brief Temperature. */ |
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169 | double temp; |
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170 | /*! \brief Annealing factor. */ |
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171 | double ann_fact; |
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172 | /*! \brief Constructor. |
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173 | * \param _max_iter maximum number of iterations |
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174 | * \param _max_no_impr maximum number of consecutive iterations which do |
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175 | * not yield a better solution |
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176 | * \param _temp initial temperature |
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177 | * \param _ann_fact annealing factor |
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178 | */ |
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179 | SimpleController(long _max_iter = 500000, long _max_no_impr = 20000, |
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180 | double _temp = 1000.0, double _ann_fact = 0.9999) : iter(0), last_impr(0), |
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181 | max_iter(_max_iter), max_no_impr(_max_no_impr), temp(_temp), |
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182 | ann_fact(_ann_fact) {} |
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183 | /*! \brief This is called when a neighbouring state gets accepted. */ |
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184 | void acceptEvent() { |
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185 | iter++; |
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186 | } |
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187 | /*! \brief This is called when the accepted neighbouring state's cost is |
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188 | * less than the best found one's. |
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189 | */ |
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190 | void improveEvent() { |
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191 | last_impr = iter; |
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192 | } |
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193 | /*! \brief This is called when a neighbouring state gets rejected. */ |
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194 | void rejectEvent() { |
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195 | iter++; |
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196 | } |
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197 | /*! \brief Decides whether to continue the annealing process or not. Also |
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198 | * decreases the temperature. */ |
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199 | bool next() { |
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200 | temp *= ann_fact; |
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201 | bool quit = (iter > max_iter) || (iter - last_impr > max_no_impr); |
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202 | return !quit; |
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203 | } |
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204 | /*! \brief Decides whether to accept the current solution or not. */ |
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205 | bool accept() { |
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206 | double cost_diff = base->getPrevCost() - base->getCurrCost(); |
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207 | if (cost_diff < 0.0) { |
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208 | bool ret = drand48() <= exp(cost_diff / temp); |
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209 | return ret; |
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210 | } |
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211 | else { |
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212 | return true; |
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213 | } |
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214 | } |
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215 | }; |
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216 | |
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217 | /*! \brief A controller with preset running time for the simulated annealing |
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218 | * class. |
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219 | * |
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220 | * With this controller you can set the running time of the annealing |
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221 | * process in advance. It works the following way: the controller measures |
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222 | * a kind of divergence. The divergence is the difference of the average |
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223 | * cost of the recently found solutions the cost of the best found one. In |
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224 | * case this divergence is greater than a given threshold, then we decrease |
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225 | * the annealing factor, that is we cool the system faster. In case the |
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226 | * divergence is lower than the threshold, then we increase the temperature. |
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227 | * The threshold is a function of the elapsed time which reaches zero at the |
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228 | * desired end time. |
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229 | */ |
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230 | class AdvancedController : public SimAnnBase::Controller { |
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231 | private: |
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232 | Timer timer; |
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233 | /*! \param time the elapsed time in seconds */ |
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234 | virtual double threshold(double time) { |
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235 | return (-1.0) * start_threshold / end_time * time + start_threshold; |
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236 | } |
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237 | public: |
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238 | double alpha; |
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239 | double beta; |
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240 | double gamma; |
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241 | /*! \brief The time at the end of the algorithm. */ |
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242 | double end_time; |
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243 | /*! \brief The time at the start of the algorithm. */ |
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244 | double start_time; |
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245 | /*! \brief Starting threshold. */ |
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246 | double start_threshold; |
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247 | /*! \brief Average cost of recent solutions. */ |
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248 | double avg_cost; |
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249 | /*! \brief Temperature. */ |
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250 | double temp; |
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251 | /*! \brief Annealing factor. */ |
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252 | double ann_fact; |
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253 | /*! \brief Initial annealing factor. */ |
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254 | double init_ann_fact; |
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255 | bool warmup; |
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256 | /*! \brief Constructor. |
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257 | * \param _end_time running time in seconds |
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258 | * \param _alpha parameter used to calculate the running average |
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259 | * \param _beta parameter used to decrease the annealing factor |
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260 | * \param _gamma parameter used to increase the temperature |
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261 | * \param _ann_fact initial annealing factor |
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262 | */ |
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263 | AdvancedController(double _end_time, double _alpha = 0.2, |
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264 | double _beta = 0.9, double _gamma = 1.6, double _ann_fact = 0.9999) : |
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265 | alpha(_alpha), beta(_beta), gamma(_gamma), end_time(_end_time), |
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266 | ann_fact(_ann_fact), init_ann_fact(_ann_fact), warmup(true) {} |
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267 | void init() { |
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268 | avg_cost = base->getCurrCost(); |
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269 | } |
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270 | /*! \brief This is called when a neighbouring state gets accepted. */ |
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271 | void acceptEvent() { |
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272 | avg_cost = alpha * base->getCurrCost() + (1.0 - alpha) * avg_cost; |
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273 | if (warmup) { |
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274 | static int cnt = 0; |
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275 | cnt++; |
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276 | if (cnt >= 100) { |
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277 | // calculate starting threshold and starting temperature |
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278 | start_threshold = 5.0 * fabs(base->getBestCost() - avg_cost); |
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279 | temp = 10000.0; |
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280 | warmup = false; |
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281 | timer.reset(); |
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282 | } |
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283 | } |
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284 | } |
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285 | /*! \brief Decides whether to continue the annealing process or not. */ |
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286 | bool next() { |
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287 | if (warmup) { |
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288 | return true; |
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289 | } |
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290 | else { |
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291 | double elapsed_time = timer.getRealTime(); |
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292 | if (fabs(avg_cost - base->getBestCost()) > threshold(elapsed_time)) { |
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293 | // decrease the annealing factor |
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294 | ann_fact *= beta; |
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295 | } |
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296 | else { |
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297 | // increase the temperature |
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298 | temp *= gamma; |
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299 | // reset the annealing factor |
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300 | ann_fact = init_ann_fact; |
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301 | } |
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302 | temp *= ann_fact; |
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303 | return elapsed_time < end_time; |
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304 | } |
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305 | } |
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306 | /*! \brief Decides whether to accept the current solution or not. */ |
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307 | bool accept() { |
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308 | if (warmup) { |
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309 | // we accept eveything during the "warm up" phase |
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310 | return true; |
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311 | } |
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312 | else { |
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313 | double cost_diff = base->getPrevCost() - base->getCurrCost(); |
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314 | if (cost_diff < 0.0) { |
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315 | return (drand48() <= exp(cost_diff / temp)); |
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316 | } |
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317 | else { |
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318 | return true; |
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319 | } |
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320 | } |
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321 | } |
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322 | }; |
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323 | |
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324 | /// @} |
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325 | |
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326 | } |
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327 | |
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328 | #endif |
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