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 best_cost; |
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20 | double prev_cost; |
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21 | double prev_prev_cost; |
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22 | |
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23 | virtual void mutate() = 0; |
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24 | virtual void revert() = 0; |
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25 | virtual void saveAsBest() = 0; |
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26 | public: |
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27 | SimAnnBase() { |
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28 | best_cost = prev_cost = prev_prev_cost = INFTY; |
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29 | } |
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30 | void setController(Controller &_controller) { |
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31 | controller = &_controller; |
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32 | controller->setBase(this); |
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33 | } |
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34 | double getCurrCost() const { return curr_cost; } |
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35 | double getPrevCost() const { return prev_cost; } |
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36 | double getBestCost() const { return best_cost; } |
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37 | void run() { |
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38 | controller->init(); |
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39 | do { |
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40 | mutate(); |
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41 | if (controller->accept()) { |
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42 | controller->acceptEvent(); |
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43 | if (curr_cost < best_cost) { |
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44 | saveAsBest(); |
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45 | controller->improveEvent(); |
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46 | } |
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47 | } |
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48 | else { |
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49 | revert(); |
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50 | controller->rejectEvent(); |
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51 | } |
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52 | } while (controller->next()); |
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53 | } |
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54 | |
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55 | /*! \brief A base class for controllers. */ |
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56 | class Controller { |
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57 | public: |
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58 | SimAnnBase *base; |
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59 | virtual void init() {} |
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60 | /*! \brief This is called when a neighbouring state gets accepted. */ |
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61 | virtual void acceptEvent() {} |
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62 | /*! \brief This is called when the accepted neighbouring state's cost is |
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63 | * less than the best found one's. |
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64 | */ |
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65 | virtual void improveEvent() {} |
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66 | /*! \brief This is called when a neighbouring state gets rejected. */ |
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67 | virtual void rejectEvent() {} |
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68 | virtual void setBase(SimAnnBase *_base) { base = _base; } |
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69 | /*! */ |
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70 | virtual bool next() = 0; |
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71 | /*! */ |
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72 | virtual bool accept() = 0; |
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73 | }; |
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74 | }; |
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75 | |
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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 | curr_cost = curr_ent->getCost(); |
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87 | } |
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88 | E getBestEntity() { return *best_ent; } |
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89 | void mutate() { |
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90 | prev_prev_cost = prev_cost; |
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91 | prev_cost = curr_cost; |
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92 | curr_ent->mutate(); |
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93 | curr_cost = curr_ent->getCost(); |
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94 | } |
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95 | void revert() { |
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96 | curr_ent->revert(); |
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97 | curr_cost = prev_cost; |
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98 | prev_cost = prev_prev_cost; |
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99 | } |
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100 | void saveAsBest() { |
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101 | delete(best_ent); |
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102 | best_ent = new E(*curr_ent); |
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103 | best_cost = curr_cost; |
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104 | } |
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105 | }; |
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106 | |
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107 | class EntitySkeleton { |
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108 | public: |
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109 | /*! \return the cost of the entity */ |
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110 | double getCost() { return 0.0; } |
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111 | /*! \brief Makes a minor change to the entity. */ |
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112 | void mutate() {} |
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113 | /*! \brief Restores the entity to its previous state i.e. reverts the |
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114 | * effects of the last mutate. |
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115 | */ |
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116 | void revert() {} |
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117 | }; |
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118 | |
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119 | /*! \brief A simple controller for the simulated annealing class. |
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120 | * \todo Find a way to set the various parameters. |
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121 | */ |
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122 | class SimpleController : public SimAnnBase::Controller { |
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123 | public: |
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124 | long iter, last_impr, max_iter, max_no_impr; |
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125 | double temp, ann_fact; |
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126 | /*! \param _max_iter maximum number of iterations |
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127 | * \param _max_no_impr maximum number of consecutive iterations which do |
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128 | * not yield a better solution |
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129 | * \param _temp initial temperature |
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130 | * \param _ann_fact annealing factor |
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131 | */ |
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132 | SimpleController(long _max_iter = 500000, long _max_no_impr = 20000, |
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133 | double _temp = 1000.0, double _ann_fact = 0.9999) : iter(0), last_impr(0), |
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134 | max_iter(_max_iter), max_no_impr(_max_no_impr), temp(_temp), |
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135 | ann_fact(_ann_fact) {} |
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136 | void acceptEvent() { |
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137 | iter++; |
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138 | } |
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139 | void improveEvent() { |
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140 | last_impr = iter; |
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141 | } |
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142 | void rejectEvent() { |
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143 | iter++; |
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144 | } |
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145 | bool next() { |
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146 | temp *= ann_fact; |
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147 | bool quit = (iter > max_iter) || (iter - last_impr > max_no_impr); |
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148 | return !quit; |
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149 | } |
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150 | bool accept() { |
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151 | double cost_diff = base->getPrevCost() - base->getCurrCost(); |
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152 | if (cost_diff < 0.0) { |
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153 | bool ret = drand48() <= exp(cost_diff / temp); |
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154 | return ret; |
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155 | } |
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156 | else { |
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157 | return true; |
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158 | } |
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159 | } |
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160 | }; |
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161 | |
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162 | /*! \brief A controller with preset running time for the simulated annealing |
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163 | * class. |
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164 | * \todo Find a better name. |
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165 | */ |
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166 | class AdvancedController : public SimAnnBase::Controller { |
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167 | private: |
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168 | Timer timer; |
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169 | /*! \param time the elapsed time in seconds */ |
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170 | virtual double threshold(double time) { |
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171 | // 1 / log(x) |
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172 | /* |
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173 | static double xm = 5.0 / end_time; |
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174 | static double ym = start_threshold / (1 / log(1.2) - 1 / log(5.0 + 1.2)); |
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175 | return ym * (1 / log(xm * time + 1.2) - 1 / log(5.0 + 1.2)); |
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176 | */ |
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177 | return (-1.0) * start_threshold / end_time * time + start_threshold; |
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178 | } |
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179 | public: |
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180 | double alpha, beta, gamma; |
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181 | double end_time, start_time; |
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182 | double start_threshold; |
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183 | double avg_cost; |
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184 | double temp, ann_fact; |
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185 | bool warmup; |
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186 | /*! \param _end_time running time in seconds |
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187 | * \param _alpha parameter used to calculate the running average |
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188 | * \param _beta parameter used to decrease the annealing factor |
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189 | * \param _gamma parameter used to increase the temperature |
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190 | */ |
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191 | AdvancedController(double _end_time, double _alpha = 0.2, |
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192 | double _beta = 0.9, double _gamma = 1.6) : alpha(_alpha), beta(_beta), |
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193 | gamma(_gamma), end_time(_end_time), ann_fact(0.99999999), warmup(true) {} |
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194 | void init() { |
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195 | avg_cost = base->getCurrCost(); |
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196 | } |
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197 | void acceptEvent() { |
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198 | avg_cost = alpha * base->getCurrCost() + (1.0 - alpha) * avg_cost; |
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199 | if (warmup) { |
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200 | static int cnt = 0; |
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201 | cnt++; |
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202 | if (cnt >= 100) { |
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203 | // calculate starting threshold and starting temperature |
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204 | start_threshold = 5.0 * fabs(base->getBestCost() - avg_cost); |
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205 | //temp = max_cost_diff / log(0.5); |
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206 | temp = 10000.0; |
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207 | warmup = false; |
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208 | timer.reset(); |
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209 | } |
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210 | } |
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211 | } |
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212 | void improveEvent() { |
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213 | } |
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214 | void rejectEvent() { |
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215 | } |
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216 | bool next() { |
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217 | if (warmup) { |
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218 | return true; |
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219 | } |
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220 | else { |
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221 | double elapsed_time = timer.getRealTime(); |
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222 | if (fabs(avg_cost - base->getBestCost()) > threshold(elapsed_time)) { |
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223 | // decrease the annealing factor |
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224 | ann_fact *= beta; |
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225 | } |
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226 | else { |
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227 | // increase the temperature |
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228 | temp *= gamma; |
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229 | ann_fact = 0.99999999; |
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230 | } |
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231 | temp *= ann_fact; |
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232 | return elapsed_time < end_time; |
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233 | } |
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234 | } |
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235 | bool accept() { |
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236 | if (warmup) { |
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237 | // we accept eveything during the "warm up" phase |
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238 | return true; |
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239 | } |
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240 | else { |
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241 | double cost_diff = base->getPrevCost() - base->getCurrCost(); |
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242 | if (cost_diff < 0.0) { |
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243 | return (drand48() <= exp(cost_diff / temp)); |
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244 | } |
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245 | else { |
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246 | return true; |
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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 | |
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252 | } |
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253 | |
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254 | #endif |
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