1 | #ifndef LEMON_SIMANN_H |
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2 | #define LEMON_SIMANN_H |
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3 | |
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4 | #include <cstdlib> |
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5 | #include <cmath> |
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6 | #include <lemon/time_measure.h> |
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7 | |
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8 | namespace lemon { |
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9 | |
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10 | const double INFTY = 1e24; |
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11 | |
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12 | class SimAnnBase { |
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13 | public: |
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14 | class Controller; |
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15 | private: |
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16 | Controller *controller; |
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17 | protected: |
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18 | double curr_cost; |
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19 | double prev_cost; |
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20 | double best_cost; |
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21 | |
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22 | virtual void mutate() = 0; |
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23 | virtual void revert() = 0; |
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24 | virtual void saveAsBest() = 0; |
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25 | public: |
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26 | SimAnnBase() { |
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27 | curr_cost = prev_cost = best_cost = INFTY; |
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28 | } |
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29 | void setController(Controller &_controller) { |
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30 | controller = &_controller; |
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31 | controller->setBase(this); |
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32 | } |
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33 | double getCurrCost() const { return curr_cost; } |
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34 | double getPrevCost() const { return prev_cost; } |
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35 | double getBestCost() const { return best_cost; } |
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36 | void run() { |
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37 | controller->init(); |
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38 | do { |
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39 | mutate(); |
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40 | if (controller->accept()) { |
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41 | controller->acceptEvent(); |
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42 | if (curr_cost < best_cost) { |
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43 | saveAsBest(); |
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44 | controller->improveEvent(); |
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45 | } |
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46 | } |
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47 | else { |
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48 | revert(); |
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49 | controller->rejectEvent(); |
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50 | } |
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51 | } while (controller->next()); |
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52 | } |
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53 | |
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54 | /*! \brief A base class for controllers. */ |
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55 | class Controller { |
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56 | public: |
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57 | SimAnnBase *base; |
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58 | virtual void init() {} |
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59 | /*! \brief This is called when a neighbouring state gets accepted. */ |
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60 | virtual void acceptEvent() {} |
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61 | /*! \brief This is called when the accepted neighbouring state's cost is |
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62 | * less than the best found one's. |
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63 | */ |
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64 | virtual void improveEvent() {} |
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65 | /*! \brief This is called when a neighbouring state gets rejected. */ |
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66 | virtual void rejectEvent() {} |
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67 | virtual void setBase(SimAnnBase *_base) { base = _base; } |
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68 | /*! */ |
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69 | virtual bool next() = 0; |
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70 | /*! */ |
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71 | virtual bool accept() = 0; |
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72 | }; |
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73 | }; |
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74 | |
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75 | /*! \todo atgondolni mi is ez a prev_cost */ |
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76 | template <typename E> |
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77 | class SimAnn : public SimAnnBase { |
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78 | private: |
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79 | E *curr_ent; |
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80 | E *best_ent; |
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81 | public: |
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82 | SimAnn() : SimAnnBase() {} |
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83 | void setEntity(E &ent) { |
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84 | curr_ent = new E(ent); |
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85 | best_ent = new E(ent); |
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86 | } |
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87 | E getBestEntity() { return *best_ent; } |
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88 | void mutate() { |
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89 | prev_cost = curr_cost; |
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90 | curr_cost = curr_ent->mutate(); |
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91 | } |
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92 | void revert() { |
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93 | curr_ent->revert(); |
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94 | curr_cost = prev_cost; |
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95 | } |
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96 | void saveAsBest() { |
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97 | *best_ent = *curr_ent; |
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98 | best_cost = curr_cost; |
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99 | } |
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100 | }; |
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101 | |
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102 | class EntitySkeleton { |
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103 | public: |
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104 | /*! \brief Makes a minor change to the entity. |
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105 | * \return the new cost |
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106 | */ |
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107 | double mutate() { return 0.0; } |
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108 | /*! \brief Restores the entity to its previous state i.e. reverts the |
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109 | * effects of the last mutate. |
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110 | */ |
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111 | void revert() {} |
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112 | }; |
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113 | |
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114 | /*! \brief A simple controller for the simulated annealing class. |
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115 | * \todo Find a way to set the various parameters. |
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116 | */ |
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117 | class SimpleController : public SimAnnBase::Controller { |
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118 | public: |
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119 | long iter, last_impr, max_iter, max_no_impr; |
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120 | double temp, ann_fact; |
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121 | /*! \param _max_iter maximum number of iterations |
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122 | * \param _max_no_impr maximum number of consecutive iterations which do |
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123 | * not yield a better solution |
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124 | * \param _temp initial temperature |
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125 | * \param _ann_fact annealing factor |
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126 | */ |
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127 | SimpleController(long _max_iter = 500000, long _max_no_impr = 20000, |
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128 | double _temp = 1000, double _ann_fact = 0.9999) : iter(0), last_impr(0), |
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129 | max_iter(_max_iter), max_no_impr(_max_no_impr), temp(_temp), |
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130 | ann_fact(_ann_fact) {} |
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131 | void acceptEvent() { |
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132 | iter++; |
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133 | } |
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134 | void improveEvent() { |
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135 | last_impr = iter; |
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136 | } |
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137 | void rejectEvent() { |
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138 | iter++; |
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139 | } |
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140 | bool next() { |
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141 | temp *= ann_fact; |
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142 | bool quit = (iter > max_iter) || (iter - last_impr > max_no_impr); |
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143 | return !quit; |
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144 | } |
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145 | bool accept() { |
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146 | double cost_diff = base->getPrevCost() - base->getCurrCost(); |
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147 | if (cost_diff < 0.0) { |
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148 | return (drand48() <= exp(cost_diff / temp)); |
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149 | } |
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150 | else { |
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151 | return true; |
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152 | } |
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153 | } |
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154 | }; |
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155 | |
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156 | /*! \brief A controller with preset running time for the simulated annealing |
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157 | * class. |
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158 | * \todo Find a better name. |
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159 | */ |
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160 | class AdvancedController : public SimAnnBase::Controller { |
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161 | private: |
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162 | Timer timer; |
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163 | /*! \param time the elapsed time in seconds */ |
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164 | virtual double threshold(double time) { |
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165 | // this is the function 1 / log(x) scaled and offset |
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166 | static double xm = 5.0 / end_time; |
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167 | static double ym = start_threshold / (1 / log(1.2) - 1 / log(5.0 + 1.2)); |
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168 | return ym * (1 / log(xm * time + 1.2) - 1 / log(5.0 + 1.2)); |
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169 | } |
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170 | public: |
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171 | double alpha, beta, gamma; |
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172 | double end_time, start_time; |
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173 | double start_threshold; |
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174 | double avg_cost; |
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175 | double temp, ann_fact; |
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176 | bool warmup; |
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177 | long iter; |
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178 | /*! \param _end_time running time in seconds |
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179 | * \param _alpha parameter used to calculate the running average |
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180 | * \param _beta parameter used to decrease the annealing factor |
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181 | * \param _gamma parameter used to increase the temperature |
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182 | */ |
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183 | AdvancedController(double _end_time, double _alpha = 0.2, |
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184 | double _beta = 0.9, double _gamma = 1.2) : alpha(_alpha), beta(_beta), |
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185 | gamma(_gamma), end_time(_end_time), ann_fact(0.9999), warmup(true), |
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186 | iter(0) {} |
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187 | void init() { |
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188 | avg_cost = base->getCurrCost(); |
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189 | } |
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190 | void acceptEvent() { |
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191 | avg_cost = alpha * base->getCurrCost() + (1.0 - alpha) * avg_cost; |
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192 | iter++; |
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193 | } |
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194 | void improveEvent() { |
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195 | } |
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196 | void rejectEvent() { |
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197 | iter++; |
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198 | } |
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199 | bool next() { |
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200 | if (warmup) { |
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201 | static double max_cost_diff = 0.0; |
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202 | double cost_diff = base->getCurrCost() - base->getPrevCost(); |
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203 | // jo ez igy egyaltalan? -> prev_cost |
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204 | if ((cost_diff > 0.0) && (cost_diff > max_cost_diff)) { |
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205 | max_cost_diff = cost_diff; |
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206 | } |
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207 | // How to set the starting temperature when all the 100 first |
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208 | // iterations improve the solution? |
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209 | if (iter > 100) { |
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210 | // calculate starting threshold and starting temperature |
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211 | start_threshold = fabs(base->getBestCost() - avg_cost); |
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212 | temp = exp(max_cost_diff) / 0.5; |
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213 | warmup = false; |
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214 | timer.reset(); |
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215 | } |
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216 | return true; |
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217 | } |
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218 | else { |
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219 | double elapsed_time = timer.getRealTime(); |
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220 | if (fabs(avg_cost - base->getBestCost()) > threshold(elapsed_time)) { |
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221 | // decrease the annealing factor |
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222 | ann_fact *= beta; |
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223 | } |
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224 | else { |
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225 | // increase the temperature |
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226 | temp *= gamma; |
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227 | } |
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228 | temp *= ann_fact; |
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229 | return elapsed_time < end_time; |
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230 | } |
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231 | } |
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232 | bool accept() { |
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233 | if (warmup) { |
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234 | // we accept eveything during the "warm up" phase |
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235 | return true; |
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236 | } |
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237 | else { |
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238 | double cost_diff = base->getPrevCost() - base->getCurrCost(); |
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239 | if (cost_diff < 0.0) { |
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240 | return (drand48() <= exp(cost_diff / temp)); |
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241 | } |
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242 | else { |
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243 | return true; |
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244 | } |
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245 | } |
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246 | } |
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247 | }; |
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248 | |
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249 | } |
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250 | |
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251 | #endif |
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