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Graph.cpp
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Graph.cpp
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#include "Graph.h"
#include <thread>
#define INT 99999
#define timer_cout std::cout
using namespace std;
/**************************************************************************************************
* Defining the Graph's methods for second part
**************************************************************************************************/
// Constructor
Graph::Graph(int order, bool directed, bool weighted_edge, bool weighted_node)
{
this->order = order;
this->directed = directed;
this->weighted_edge = weighted_edge;
this->weighted_node = weighted_node;
this->first_node = this->last_node = nullptr;
this->number_edges = 0;
}
// Destructor
Graph::~Graph()
{
Node *next_node = this->first_node;
while (next_node != nullptr)
{
next_node->removeAllEdges(this->directed);
Node *aux_node = next_node->getNextNode();
delete next_node;
next_node = aux_node;
}
}
// Getters
int Graph::getOrder()
{
return this->order;
}
int Graph::getNumberEdges()
{
return this->number_edges;
}
bool Graph::getDirected()
{
return this->directed;
}
bool Graph::getWeightedEdge()
{
return this->weighted_edge;
}
bool Graph::getWeightedNode()
{
return this->weighted_node;
}
Node *Graph::getFirstNode()
{
return this->first_node;
}
Node *Graph::getLastNode()
{
return this->last_node;
}
void Graph::setFirstNode(Node *node)
{
this->first_node = node;
}
void Graph::setLastNode(Node *node)
{
this->last_node = node;
}
// Other methods
/*
The outdegree attribute of nodes is used as a counter for the number of edges in the graph.
This allows the correct updating of the numbers of edges in the graph being directed or not.
*/
void Graph::insertNode(int id)
{
Node *next;
Node *aux = nullptr;
if (this->getFirstNode() == nullptr)
{
this->first_node = new Node(id);
this->last_node = this->getFirstNode();
}
else
{
if (!this->searchNode(id))
{
Node *node = new Node(id);
node->setNextNode(nullptr);
this->last_node->setNextNode(node);
this->last_node = node;
}
}
}
void Graph::insertEdge(int id, int target_id, float weight)
{
if(getNode(id) == nullptr)
{
insertNode(id);
}
if(getNode(target_id) == nullptr)
{
insertNode(target_id);
}
if(!getNode(id)->searchEdge(target_id)){
if(directed)
{
getNode(id)->insertEdge(target_id, weight);
getNode(id)->incrementOutDegree();
getNode(target_id)->incrementInDegree();
this->number_edges++;
} else {
//Não possui incremento de grau //TODO
getNode(id)->insertEdge(target_id, weight);
if(id == target_id)
getNode(id)->in_degree+=2;
else
getNode(id)->in_degree++;
if(!getNode(target_id)->searchEdge(id))
{
getNode(target_id)->insertEdge(id, weight);
getNode(target_id)->in_degree++;
}
this->number_edges++;
}
}
}
void Graph::removeEdge(int id, int target_id)
{
this->number_edges -= getNode(id)->removeEdge(target_id, directed, getNode(target_id));
}
bool Graph::searchNode(int id)
{
if (this->first_node != nullptr)
{
for (Node *aux = this->first_node; aux != nullptr; aux = aux->getNextNode())
{
if (aux->getId() == id)
return true;
}
}
return false;
}
Node *Graph::getNode(int id)
{
if (this->first_node != nullptr)
{
for (Node *aux = this->first_node; aux != nullptr; aux = aux->getNextNode())
{
if (aux->getId() == id)
return aux;
}
}
return nullptr;
}
//biggest ratio between the node's degree and it's weight
double lambda3(Graph* graph,int node_id,map <int,bool> &in_solution){
Node * node = graph->getNode(node_id);
return node->getInDegree()/node->getWeight();
}
// biggest ratio between the sum of the weights of the non-dominated neighbors and the node weight
double lambda4(Graph* graph, int node_id,map <int,bool> &in_solution){
Node* node = graph->getNode(node_id);
int neighbor_weight = 0;
for(Edge* edge = node->getFirstEdge(); edge != nullptr; edge = edge->getNextEdge()){
if(!in_solution[edge->getTargetId()])
neighbor_weight += graph->getNode(edge->getTargetId())->getWeight();
}
return neighbor_weight/node->getWeight();
}
// biggest ratio between the product of the non-dominated neighbor count and sum the of weights with the node weight
double lambda5(Graph* graph,int node_id,map <int,bool> &in_solution){
Node * node = graph->getNode(node_id);
int neighbor_weight = 0;
int non_dominated_neighbors = 0;
for(Edge* edge = node->getFirstEdge(); edge != nullptr; edge = edge->getNextEdge()){
if(!in_solution[edge->getTargetId()]){
neighbor_weight += graph->getNode(edge->getTargetId())->getWeight();
non_dominated_neighbors++;
}
}
return (non_dominated_neighbors*neighbor_weight)/node->getWeight();
}
// biggest sum of non-dominated neighbor weight
double lambda6(Graph* graph, int node_id,map <int,bool> &in_solution){
Node* node = graph->getNode(node_id);
int neighbor_weight = 0;
for(Edge* edge = node->getFirstEdge(); edge != nullptr; edge = edge->getNextEdge()){
if(!in_solution[edge->getTargetId()])
neighbor_weight += graph->getNode(edge->getTargetId())->getWeight();
}
return neighbor_weight;
}
priority_queue<pair<double,int>> heuristic(Graph* graph, double (lambda)(Graph*,int,map<int,bool>&), map <int,bool> &in_solution){
priority_queue<pair<double,int>>node_degrees;
Node * node = graph->getFirstNode();
double heuristic_value = 0;
while(node!=nullptr){
heuristic_value = lambda(graph,node->getId(),in_solution);
node_degrees.push(make_pair(heuristic_value, node->getId()));
node = node->getNextNode();
}
return node_degrees;
}
vector<int> heuristic2(Graph *graph,map <int,bool> &in_solution)
{
priority_queue<pair<double, int>> aux = heuristic(graph,lambda4,in_solution);
vector<int> vet;
while(!aux.empty())
{
vet.push_back(aux.top().second);
aux.pop();
}
return vet;
}
unsigned int rNode(int min, int max)
{
std::random_device rd;
std::mt19937_64 e{rd()};
std::uniform_int_distribution<> dist{min, max};
unsigned int randomNumber = dist(e);
return randomNumber;
}
//Greedy Constructive Algorithm
set<pair<int,int>> Graph::GreedyConstructive(){
// set containing each node and its weight
set<pair<int,int>> auxSolutionSet;
// map to verify if node is in solution
map<int,bool> in_solution;
for(int i = 1; i < this->order; i++){
in_solution.insert(make_pair(i,false));
}
// get first node
Node * node = this->getFirstNode();
//max heap to get node with highest degree
priority_queue<pair<double,int>> node_degrees = heuristic(this,lambda4,in_solution);
bool viable = false;
int check = 0;
int heuristic_node = node_degrees.top().second;
node_degrees.pop();
while(!viable){
for(Edge* edge =this->getNode(heuristic_node)->getFirstEdge(); edge != nullptr; edge = edge->getNextEdge()){
if(!in_solution[edge->getTargetId()]){
auxSolutionSet.insert(make_pair(heuristic_node,getNode(heuristic_node)->getWeight()));
in_solution[heuristic_node] = true;
}
in_solution[edge->getTargetId()] = true;
}
for(int i = 1; i < this->order; i++){
if(in_solution[i]){
check++;
}
}
if(check == this->order-1){
viable = true;
} else {
check = 0;
node_degrees = heuristic(this,lambda5,in_solution);
}
heuristic_node = node_degrees.top().second;
node_degrees.pop();
}
return auxSolutionSet;
}
set<pair<int,int>> Graph::GreedyRandomizedAdaptive(double alpha, int numIter){
//!Timer
std::chrono::time_point<std::chrono::high_resolution_clock> start, end;
start = chrono::high_resolution_clock::now();
// set for the best solution
set<pair<int,int>> bestSolutionSet;
// set for the current solution
set<pair<int,int>> auxSolutionSet;
// map to verify if node is in solution
map<int,bool> in_solution;
int i = 1, currentWeight = 0, bestWeight;
unsigned int k = 0;
while (i <= numIter) {
// candidate list is ordered according to biggest ratio between the weights sum of the
// non-dominated neighbors and the weight of the node
vector<int> candidateList = heuristic2(this,in_solution);
// initialize solution set
for(int i = 1; i < this->order; i++){
in_solution.insert(make_pair(i,false));
}
while (!candidateList.empty()) {
// get random node from candidate list
k = rNode(0, trunc(alpha* (float)candidateList.size()));
sort(candidateList.begin(), candidateList.end(), greater<int>());
int randomNode = candidateList[k];
// if node is not in solution yet
if (!in_solution[randomNode]) {
// add node to solution
auxSolutionSet.insert(make_pair(randomNode,getNode(randomNode)->getWeight()));
in_solution[randomNode] = true;
currentWeight += getNode(randomNode)->getWeight();
}
// remove node from candidate list
candidateList.erase(candidateList.begin()+k);
// remove dominated nodes from candidate list
for (int j = 0; j < candidateList.size(); j++) {
Node * candidate = this->getNode(candidateList[j]);
if (candidate->searchEdge(randomNode)) {
candidateList.erase(candidateList.begin()+j);
}
}
}
// if current solution is better than best solution
if ((i == 1 || currentWeight < bestWeight) && !auxSolutionSet.empty()) {
bestSolutionSet.clear();
bestSolutionSet.swap(auxSolutionSet);
bestWeight = currentWeight; //?soma-se 1 aqui??
}
i++;
currentWeight = 0;
auxSolutionSet.clear();
in_solution.clear();
}
end = chrono::high_resolution_clock::now();
int elapsed_seconds = chrono::duration_cast<chrono::milliseconds>(end-start).count();
return bestSolutionSet;
}
void Graph::updateProbabilities(vector<double>*probabilities, vector<double>alphas, int bestWeight, vector<pair<double,int>>avgWeights) {
// according to section 3.1 Reactive GRASP on Handbook of Metaheuristics (check if it's correct)
vector <double> q;
double sum = 0, qi;
for (int i = 0; i < alphas.size(); i++) {
if (avgWeights.at(i).first != 0)
qi = bestWeight/(double)avgWeights.at(i).first;
else
qi = 1.0/alphas.size();
q.push_back(qi);
sum += qi;
}
for (int i = 0; i < alphas.size(); i++) {
if (q.at(i) != 0)
probabilities->at(i) = (q.at(i) / sum);
}
q.clear();
}
double Graph::chooseAlpha(vector<double>* probabilities, vector<double> alphas) {
// choose alpha according to probabilities
double alpha;
double highest = 0;
for (int i = 0; i < probabilities->size(); i++) {
if (probabilities->at(i) > highest) {
highest = probabilities->at(i);
alpha = alphas[i];
}
}
return alpha;
}
void Graph::updateAvgWeights(vector<pair<double,int>>& avgWeights, vector<double> alphas, double alpha, int currentWeight) {
// update average weights when alphas[j] == alpha
if (currentWeight == 0) return;
for (int j = 0; j < alphas.size(); j++) {
if (alphas[j] == alpha) {
avgWeights.at(j).second++;
int qtd = avgWeights.at(j).second;
double avg = avgWeights.at(j).first;
avgWeights.at(j).first = (avg*(qtd-1) + currentWeight)/qtd;
break;
}
}
}
set<pair<int,int>> Graph::GreedyRandomizedReactive(vector<double> alphas, int numIter, int block_size){
// list of probabilities
vector<double> probabilities(alphas.size(), 1.0/alphas.size());
// average of weights for each alpha
vector<pair<double,int>> avgWeights(alphas.size(), make_pair(0,0));
// set for the best solution
set<pair<int,int>> bestSolutionSet;
// set for the current solution
set<pair<int,int>> auxSolutionSet;
// map to verify if node is in solution
map<int,bool> in_solution;
int i=1, currentWeight = 0, bestWeight = INT;
unsigned int k;
double alpha;
while (i <= numIter) {
// find first n solutions using each one of the alphas
if (i <= alphas.size())
alpha = alphas[i-1];
else {
alpha = chooseAlpha(&probabilities, alphas);
}
if (i % block_size == 0) {
// update probabilities
updateProbabilities(&probabilities, alphas, bestWeight, avgWeights);
}
// initialize solution set
for(int i = 1; i < this->order; i++){
in_solution.insert(make_pair(i,false));
}
// candidate list is ordered according to biggest ratio between the weights sum of the
// non-dominated neighbors and the weight of the node
vector<int> candidateList = heuristic2(this,in_solution);
sort(candidateList.begin(), candidateList.end(), greater<int>());
while (!candidateList.empty()) {
// exactly like the GRASP algorithm, but choosing between a set of possible alphas
k = rNode(0, trunc((1-alpha)* (float)candidateList.size()));
int randomNode = candidateList[k];
// if node is not in solution yet
if (!in_solution[randomNode]) {
// add node to solution
auxSolutionSet.insert(make_pair(randomNode,getNode(randomNode)->getWeight()));
in_solution[randomNode] = true;
currentWeight += getNode(randomNode)->getWeight();
}
// remove node from candidate list
candidateList.erase(candidateList.begin()+k);
// remove dominated nodes from candidate list
for (int j = 0; j < candidateList.size(); j++) {
Node * candidate = this->getNode(candidateList[j]);
if (candidate->searchEdge(randomNode)) {
candidateList.erase(candidateList.begin()+j);
}
}
}
updateAvgWeights(avgWeights, alphas, alpha, currentWeight);
// if current solution is better than best solution
if ((i == 1 || currentWeight < bestWeight) && !auxSolutionSet.empty()) {
bestSolutionSet.clear();
bestSolutionSet.swap(auxSolutionSet);
bestWeight = currentWeight;
}
i++;
currentWeight = 0;
auxSolutionSet.clear();
in_solution.clear();
candidateList.clear();
}
return bestSolutionSet;
}