open ... 模擬退火演算法模板

可用於解決TSP等類似問題,可直接使用.

 

#include <vector> #include <functional> class DynamicCoolingPolicy { public: DynamicCoolingPolicy() { accelerateArgument=.5; totalObjValue=0; totalObjValueSqure=0; counts=0; } void accumulate(double v) { totalObjValue+=v; totalObjValueSqure+=(v*v); counts++; } double newTemperature(double currentTemp,bool accelerate) { double stddrv=sqrt(counts*totalObjValueSqure-totalObjValue*totalObjValue/counts)/counts; currentTemp=currentTemp*exp(-accelerateArgument*currentTemp/stddrv); totalObjValue=0; totalObjValueSqure=0; counts=0; if(accelerate) accelerateArgument*=1.01; return currentTemp; } private: size_t counts; double accelerateArgument; double totalObjValueSqure; double totalObjValue; }; class StaticCoolingPolicy { public: StaticCoolingPolicy(); void accumulate(double v) { } double newTemperature(double currentTemp,bool accelerate) { return currentTemp*0.97; } }; class DynamicSelectPolicy { public: DynamicSelectPolicy(size_t neighborTypes) { for (size_t i=0;i<neighborTypes;++i) { probabilityList.push_back((i+1)*1.0/neighborTypes); } contributionCountList.assign(neighborTypes,0); contributionAmountList.assign(neighborTypes,0.1); } size_t select() const { double selProb=g_get_randomUint(1000)/1000.0; for (size_t i=0;i<probabilityList.size();++i) { if(selProb<probabilityList[i]+0.0001) { return i; } } return 0; } void accumulate(size_t ii,double contribution) { ++contributionCountList[ii]; contributionAmountList[ii]+=contribution; } void update() { double totalContributionOfAveg=0; for (size_t i=0;i<probabilityList.size();++i) { if(contributionCountList[i]>0) contributionAmountList[i]=contributionAmountList[i]/contributionCountList[i]; contributionAmountList[i]+=0.015; totalContributionOfAveg+=contributionAmountList[i]; } double acc=0; for (size_t i=0;i<probabilityList.size();++i) { acc+=contributionAmountList[i]/totalContributionOfAveg; probabilityList[i]=acc; contributionCountList[i]=0; contributionAmountList[i]=0; } } private: std::vector<double> probabilityList; std::vector<size_t> contributionCountList; std::vector<double> contributionAmountList; }; class EvenSelectPolicy { public: EvenSelectPolicy(size_t neighborTypes) :neighbor_types(neighborTypes) { }; size_t select() const { return g_get_randomUint(neighbor_types); } void accumulate(size_t ii,double contribution) { } void update() { } private: size_t neighbor_types; }; template<class RunSolution,class _CoolingPolicy,class _SelectPolicy,class MonitorType> void rapidSa(RunSolution* runSln,boost::function<void(MonitorType)> monitor,double terminateTemp) { MonitorType monitordata; double currentTemp=runSln->initTemperature(); size_t maxInners=runSln->maxInners(); size_t currentInners=0; double deltaObjValue=0; double bestObjValue=1e10; _CoolingPolicy coolingPolicy; _SelectPolicy selectPolicy(runSln->neighborTypes()); //probability init runSln->prepare(); while (true) { double currentObjValue=runSln->objValue(); //assert(currentObjValue>0); double prevObjValue=currentObjValue; while(true) { size_t iSel=selectPolicy.select(); runSln->randAdj(iSel); runSln->updateSln(); currentObjValue=runSln->objValue(); //assert(currentObjValue>0); coolingPolicy.accumulate(currentObjValue); deltaObjValue=currentObjValue-prevObjValue; if(deltaObjValue<=0) { prevObjValue=currentObjValue; selectPolicy.accumulate(iSel,-deltaObjValue); if(bestObjValue>currentObjValue ) { bestObjValue=currentObjValue; runSln->saveBest(monitordata); monitor(monitordata); } } else { double c = exp( -(deltaObjValue/currentTemp) ); double p = g_get_randomUint(10000)/10000.0; if(c > p ) { prevObjValue=currentObjValue; selectPolicy.accumulate(iSel,0); } else { runSln->rollBack(); } } if(currentInners>maxInners) break; else currentInners++; } if(currentTemp<terminateTemp) break; currentTemp=coolingPolicy.newTemperature(currentTemp,false); currentInners = 0; runSln->prepare(); runSln->adjPressure(); selectPolicy.update(); } runSln->loadBestScene(); }

 

enjoy it !!!