abc_algorithm/myAlgorithm.cpp at master · Pr3DaSniP/abc_algorithm · GitHub

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#include "myAlgorithm.h"

#include <fstream>

myAlgorithm::myAlgorithm() :
	d_population{}
{
	initializePopulation();
	calculeProbabilite();
	FindBestSolution();
}

//
// GEILLER Valentin
//
void myAlgorithm::initializePopulation() {
	d_population.resize(pop_size);
	for (int i = 0; i < pop_size; ++i) {
		d_population[i] = create_new_individual();
	}
}

//
// MUHIRWA GABO Oreste
//
double myAlgorithm::maxFitness() const {
	double max = d_population[0].getFitnessValue();
	for (int i = 0; i < d_population.size(); ++i) {
		if (max <= d_population[i].getFitnessValue()) {
			max = d_population[i].getFitnessValue();
		}
	}
	return max;
}

//
// MUHIRWA GABO Oreste
//
int myAlgorithm::indexOfSolutionWhereSuperiorOfLimit() const {
	int index = 0;
	double max = maxFitness();
	for (int i = 0; i < d_population.size(); ++i) {
		if (d_population[i].getFitnessValue() == max)
			index = i;
	}
	return index;
}

//
// MUHIRWA GABO Oreste
//
void myAlgorithm::FindBestSolution() {
	d_bestFS = d_population[indexOfSolutionWhereSuperiorOfLimit()];
}

//
// MUHIRWA GABO Oreste
//
void myAlgorithm::calculeProbabilite() {
	d_probabilite.resize(d_population.size());
	double maxfitness = maxFitness();
	for (int i = 0; i < d_probabilite.size(); ++i) {
		double fit = d_population[i].getFitnessValue();
		d_probabilite[i] = 0.9 * (fit / maxfitness) + 0.1;
	}
}


//
//	GEILLER Valentin
//
void myAlgorithm::employedBeePhase() {
	for (int i = 0; i < NB_ABEILLES_EMPLOYE; ++i) {
		FoodSource tmpFS = d_population[i];

		int variableAChanger = generate_random_int(0,dimension-1);
		int partenaire = generate_random_int(0, pop_size - 1);

		while (partenaire == i)
			partenaire = generate_random_int(0, pop_size - 1);

		double Xi = tmpFS.getCoordAt(variableAChanger);
		double XPartner = d_population[partenaire].getCoordAt(variableAChanger);

		double phi = generate_random_double(-1, 1);
		double Xnew = newCoords(Xi, XPartner);

		verifLimit(Xnew);

		tmpFS.setCoordAt(variableAChanger, Xnew);

		auto coords = tmpFS.getCoords();

		tmpFS.setObjectifValue(fct_obj(coords));
		tmpFS.setFitnessValue(fitness(tmpFS.getObjectifValue()));

		if (greedySelection(d_population[i].getFitnessValue(), tmpFS.getFitnessValue())) {
			tmpFS.resetTrial();
			d_population[i] = tmpFS;
		}
		else {
			d_population[i].incrementeTrial();
		}
	}
}

//
// MUHIRWA GABO Oreste
//
void myAlgorithm::onLookerBeePhase() {
	calculeProbabilite();
	int foodSource = 0, onLookerBee = 0;
	while (onLookerBee <= NB_ABEILLES_SPECTATEUR) {
		double r = generate_random_double(0, 1);
		if (r < d_probabilite[foodSource]) {
			employedBeePhase();
			onLookerBee = onLookerBee + 1;
		}
		foodSource = foodSource + 1;
		if (foodSource >= d_population.size()) {
			foodSource = 0;
		}
	}
}

//
// MUHIRWA GABO Oreste
//
void myAlgorithm::scoutBeePhase() {
	auto index = indexOfSolutionWhereSuperiorOfLimit();

	FoodSource FS = create_new_individual();

	if (FS.getObjectifValue() < d_bestFS.getObjectifValue()) {
		d_population[index] = FS;
	}
}


//
// MUHIRWA GABO Oreste & GEILLER Valentin
//
double myAlgorithm::solve() {
	double meilleur = DBL_MAX;


	// GENERATION
	for (int iter = 0; iter < total_func_evals; ++iter) {

		// EMPLOYES BEES PHASE
		employedBeePhase();

		// ONLOOKER BEES PHASE
		onLookerBeePhase();

		// SCOUT BEES PHASE
		FindBestSolution();
		scoutBeePhase();

		if (meilleur > d_bestFS.getObjectifValue()) {
			meilleur = d_bestFS.getObjectifValue();
		}
	}

	std::cout << " - Meilleur : " << meilleur << endl;
	return meilleur;
}