def tourney(self):
		tourneyPop = Population(Breeder.tourneySize, False)

		for i in xrange(self.pop.size()):
			tourneyPop.addIndividual( self.pop.individuals[random.randrange(0,self.pop.size())] )

		return tourneyPop.getFittest()
	def evolve(self):
		newPop = Population(self.pop.size(), False)

		i = 0
		while i < self.pop.size():
			ind1 = self.tourney()
			ind2 = self.tourney()
			newInd = self.breed(ind1, ind2)
			newPop.addIndividual(newInd)
			i += 1

		return newPop
Пример #3
0
def generatePopulation(size=10):
    pop = Population()

    for i in range(0, size):
        tR = TravelRoute()
        tR.genRoute(cityNames)

        ind = alexTools.travelRouteToIndividual(tR, sMap)
        alexTools.determineFitnessOfTravelRoute(sMap, mCities, ind)

        pop.addIndividual(ind)

    return pop
Пример #4
0
    def GA(self, seed):
        global mutex
        global newIndividuals
        split = splitAl()
        np.random.seed(seed)
        # define população inicial
        pop = Population()
        population = pop.definePopulation(config.SIZE_POP)

        def minor(x, y):
            return x if x.get_cost() < y.get_cost() else y

        best = 0
        bestPrev = 0
        cont = 0
        timeControl = 0
        threads = []
        level = 2
        # avalie a população

        # critério de parada
        i = 0
        timeIni = time.time()
        while i < config.GEN and cont <= 2 * config.GEN_NO_EVOL and timeControl < config.TIME_TOTAL:
            tAllIni = time.time()
            bestPrev = best
            tLS = 0

            # sizePopulation = len(population)
            descendant = []
            for j in range(round(config.SIZE_DESC / 2)):
                tGenIni = time.time()
                selProbalities = pop.get_selProbabilities(
                )  # probabilidade de seleção
                # print("prob: "+str(len(selProbalities)))

                # selecione os pais

                aux = np.random.choice(population,
                                       2,
                                       replace=False,
                                       p=selProbalities)

                P1 = minor(aux[0], aux[1])

                aux = np.random.choice(population,
                                       2,
                                       replace=False,
                                       p=selProbalities)

                P2 = minor(aux[0], aux[1])

                # Crossover

                rand = np.random.random_sample()
                # print(rand)
                # print(P1)
                # print(P2)
                children = []

                if rand > 0.5:
                    children = cross.OBX1(copy.deepcopy(P1), copy.deepcopy(P2))
                else:
                    children = cross.PMX1(copy.deepcopy(P1), copy.deepcopy(P2))
                # print("children: \n")
                # print(children)
                # for a in range(2):
                #     for e1, c1 in enumerate(children[a].get_giantTour()):
                #         for e2, c2 in enumerate(children[a].get_giantTour()):
                #             if e1 != e2 and c1 == c2:
                #                 print("Elementos iguais")
                #                 exit(1)

                # Mutação

                # duas threads
                modChildren = []
                with concurrent.futures.ThreadPoolExecutor(
                        max_workers=2) as executor:
                    future_to_child = {
                        executor.submit(Mutation.mutation1, child, level):
                        child
                        for child in children
                    }
                    for future in concurrent.futures.as_completed(
                            future_to_child):
                        child = future_to_child[future]
                        try:
                            indiv = future.result()
                            modChildren.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local: %s' %
                                  (str(child), exc))

                # split
                # cluster = SplitDepots.splitByDepot(modChildren[0])
                # print(cluster)
                individual1 = split.splitLinear(modChildren[0], True)
                # individual1 = split.mountRoutes(cluster)
                # cluster = SplitDepots.splitByDepot(modChildren[1])
                # print(cluster)
                individual2 = split.splitLinear(modChildren[1], True)
                # individual2 = split.mountRoutes(cluster)

                individuals = [individual1, individual2]

                # print("individual: ")
                # print(individual1)
                # print(individual2)
                for a in range(2):
                    for ii, c1 in enumerate(individuals[a].get_giantTour()):
                        for jj, c2 in enumerate(
                                individuals[a].get_giantTour()):
                            if ii != jj and c1 == c2:
                                print("Elementos iguais na mutação")
                                exit(1)

                # Busca Local
                ILS = ils()
                # solutions = ILS.ils(individual1, 25)
                # for ii, c1 in enumerate(solutions.get_giantTour()):
                #     for jj, c2 in enumerate(solutions.get_giantTour()):
                #         if ii != jj and c1 == c2:
                #             print("Elementos iguais na ils")
                #             exit(1)

                # exit(1)
                # duas threads
                ini = time.time()
                modIndividuals = []
                LS = ls()
                individuals.append(P1)
                individuals.append(P2)
                # modIndividuals.append(LS.LS(individuals[0]))
                # modIndividuals.append(LS.LS(individuals[1]))
                with concurrent.futures.ThreadPoolExecutor(
                        max_workers=2) as executor:
                    # future_to_individual = {executor.submit(
                    #     LS.LS, ind, nMovimentations='random', where='ls', timeIni=tGenIni): ind for ind in individuals}
                    # future_to_individual = {executor.submit(
                    #     ILS.ils, ind, config.GEN_ILS, timeIni=tGenIni, timeMax=config.TIME_GEN): ind for ind in individuals}
                    future_to_individual = {
                        executor.submit(self.localSearch,
                                        ind,
                                        prob=config.PROB_LS,
                                        nMovimentations='random',
                                        timeIni=tGenIni): ind
                        for ind in individuals
                    }
                    for future in concurrent.futures.as_completed(
                            future_to_individual):
                        ind = future_to_individual[future]
                        try:
                            indiv = future.result()
                            modIndividuals.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local: %s' %
                                  (str(ind), exc))
                            traceback.print_exc()
                # print(future_to_individual)
                # print(individuals[0])
                # print(modIndividuals)
                tTotal = time.time() - ini
                tLS += tTotal
                for a in range(2):
                    for e1, c1 in enumerate(modIndividuals[a].get_giantTour()):
                        for e2, c2 in enumerate(
                                modIndividuals[a].get_giantTour()):
                            if e1 != e2 and c1 == c2:
                                print("Elementos iguais na busca local")
                                exit(1)

                # avalie a população

                for a in range(2):
                    # indivíduo diferente do resto da população
                    if self.is_different(modIndividuals[a], descendant):
                        descendant.append(modIndividuals[a])

            # inserir descendentes à população

            for desc in descendant:
                if pop.is_different(desc):
                    pop.addIndividual(desc)

            # inserir indivíduos da lista newIndividuals (se existir) à população

            # início seção crítica
            mutex.acquire()
            # print("verificar lista")
            # print(newIndividuals)
            if newIndividuals:
                # print("novo: "+ str(len(newIndividuals)))
                for ni in newIndividuals:
                    if pop.is_different(ni):
                        pop.addIndividual(ni)
                newIndividuals = []
            mutex.release()
            # fim seção crítica

            pop.sortPopulation()
            population = pop.get_population()

            # promoção - busca local first improvement de 10% da população
            ini = time.time()
            p = max(round(config.SIZE_POP * 0.1), 1)  # 10% da população
            LSBetter = lsb()

            modIndividuals = []
            individuals = []
            selProbalities = pop.get_selProbabilities(
            )  # probabilidade de seleção
            individuals = np.random.choice(population,
                                           p,
                                           replace=False,
                                           p=selProbalities)
            individuals = np.append(individuals, pop.showBestSolution())

            with concurrent.futures.ThreadPoolExecutor(
                    max_workers=4) as executor:
                # future_to_individual = {executor.submit(
                #     LSBetter.LS, ind, where='ls', timeIni=tGenIni): ind for ind in individuals}
                future_to_individual = {
                    executor.submit(self.localSearch,
                                    ind,
                                    prob=config.PROB_LS_BEST,
                                    nMovimentations='all',
                                    timeIni=tGenIni): ind
                    for ind in individuals
                }
                # future_to_individual = {executor.submit(
                #     ILS.ils, ind, config.N_REPETITIONS, timeIni=tGenIni, timeMax=config.TIME_GEN): ind for ind in individuals}
                for future in concurrent.futures.as_completed(
                        future_to_individual):
                    ind = future_to_individual[future]
                    try:
                        indiv = future.result()
                        modIndividuals.append(indiv)
                    except Exception as exc:
                        print(
                            '%s gerou uma exceção na busca local - promoção: %s'
                            % (str(ind), exc))
                        traceback.print_exc()

            # avalie a população

            for a in modIndividuals:

                # for e1, c1 in enumerate(a.get_giantTour()):
                #     for e2, c2 in enumerate(a.get_giantTour()):
                #         if e1 != e2 and c1 == c2:
                #             print("Elementos iguais na busca local - promoção")
                #             exit(1)

                # indivíduo diferente do resto da população
                if pop.is_different(a):
                    pop.addIndividual(a)

            tTotalP = time.time() - ini

            pop.sortPopulation()

            # defina a população sobrevivente

            best = pop.defineSurvivors(config.SIZE_POP)
            population = pop.get_population()

            # busca local exaustiva assícrona - best improvemment dos dois melhores indivíduos

            individuals = []
            selProbalities = pop.get_selProbabilities(
            )  # probabilidade de seleção
            individuals = np.random.choice(population, 1, p=selProbalities)
            individuals = np.append(individuals, pop.showBestSolution())
            individuals = np.append(individuals, pop.showSecondBestSolution())

            # cria threads
            for individual in individuals:
                if np.random.random_sample() < config.PROB_LS_BEST_P:
                    if th.active_count(
                    ) < 4:  # máximo 3 threads agindo de forma assíncrona
                        a = MyThread(individual, timeIni)  # inicializa thread
                        a.start()
                        threads.append(a)

            # verifica número de gerações sem melhoras

            if round(bestPrev, 9) == round(best, 9):
                cont += 1
                level += 1

                # config.SOFT_VEHICLES = True
            else:
                cont = 0
                level = 2
                # config.SOFT_VEHICLES = False

            if cont > config.GEN_NO_EVOL:
                mt = ils()
                individualM = mt.pertubation(
                    copy.deepcopy(pop.showBestSolution()), level)
                pop.addIndividual(individualM)
                pop.sortPopulation()
                aux = 0

                # verifica se há solutions na lista newIndividuos

                # início seção crítica
                mutex.acquire()
                if newIndividuals:
                    aux = 1
                    for ni in newIndividuals:
                        if pop.is_different(ni):
                            pop.addIndividual(ni)
                    pop.sortPopulation()
                newIndividuals = []
                mutex.release()
                # fim seção crítica

                if aux >= 0:
                    best = pop.defineSurvivors(config.SIZE_POP)
                    if round(bestPrev, 9) != round(best, 9):
                        cont = 0

                # print("ALERTA POPULAÇÃO PAROU DE EVOLUIR")
                # print(pop.get_population())
                # logging.debug("ALERTA POPULAÇÃO PAROU DE EVOLUIR")

            pop.sortPopulation()
            population = pop.get_population()
            tAll = time.time() - tAllIni  # tempo da geração
            timeControl = time.time() - timeIni  # tempo total

            # print("GERAÇÃO: {} - Custo: {} - Tempo LS: {} - Tempo LS Promotion: {} - Tempo Total: {}".format(i,
            #  pop.showBestSolution().get_cost(), tLS/60, tTotalP/60, tAll/60))
            # logging.debug("GERAÇÃO: {} - Custo: {} - Tempo LS: {} - Tempo LS Promotion: {} - Tempo Total: {}".format(i,
            #                                                                                                          pop.showBestSolution().get_cost(), tLS/60, tTotalP/60, tAll/60))
            i += 1

        # finalizar thread se ultrapassar o tempo limite.
        if (time.time() - timeIni) >= config.TIME_TOTAL:
            for t in threads:
                t.stop()
        else:
            for t in threads:
                t.join()

        # verificar se há indivíduos na lista newIndividuals
        if newIndividuals:
            for ni in newIndividuals:
                if pop.is_different(ni):
                    pop.addIndividual(ni)
        newIndividuals = []

        # ordena população
        pop.sortPopulation()
        # print(pop.showBestSolution().get_routes())
        # print(pop.showBestSolution().get_cost())
        # print(pop.showBestSolution().get_nRoutesByDepot())
        # self.test(pop.showBestSolution())

        # retorna melhor indivíduo
        return pop.showBestSolution()
Пример #5
0
    def GA(self,seed):
        global mutex
        global newIndividuals
        np.random.seed(seed)
        # define população inicial
        pop = Population()
        population = pop.definePopulation(config.SIZE_POP)
        def minor(x, y): return x if x.get_cost() < y.get_cost() else y
        best = 0
        bestPrev = 0
        controlPop = True
        controlPopPrev = True
        sumControl = 0
        cont = 0
        timeControl = 0
        threads = []
        # avalie a população

        # critério de parada
        i = 0
        while i < config.GEN and cont <= config.GEN_NO_EVOL and timeControl < config.TIME_TOTAL :
            tAllIni = time.time()
            bestPrev = best
            controlPopPrev = controlPop
            tLS = 0

            #sizePopulation = len(population)
            descendant = []
            for j in range(round(config.SIZE_DESC/2)):
                controlPop = True
                selProbalities = pop.get_selProbabilities() # probabilidade de seleção
                # print("pop: "+str(len(population)))
                # print("prob: "+str(len(selProbalities)))
                # selecione os pais
                aux = np.random.choice(population,2,replace=False,p=selProbalities)

                # aux1 = population[np.random.randint(len(population))]
                # aux2 = population[np.random.randint(len(population))]

                P1 = minor(aux[0], aux[1])

                aux = np.random.choice(population,2,replace=False,p=selProbalities)
                # aux1 = population[np.random.randint(len(population))]
                # aux2 = population[np.random.randint(len(population))]
                P2 = minor(aux[0], aux[1])

                # Crossover

                rand = np.random.random()
                # print(rand)
                # print(P1)
                # print(P2)
                children = []
                if rand > 0.5:
                    children = cross.OBX(copy.deepcopy(P1), copy.deepcopy(P2))
                else:
                    children = cross.PMX(copy.deepcopy(P1), copy.deepcopy(P2))
                # print("child: \n")
                # print(child)
                # for a in range(2):
                #     for e1, c1 in enumerate(children[a]):
                #         for e2, c2 in enumerate(children[a]):
                #             if e1 != e2 and c1 == c2:
                #                 print("Elementos iguais")
                #                 exit(1)

                # Mutação

                # duas threads
                modChildren =  []
                with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
                    future_to_child = {executor.submit(Mutation.mutation,child): child for child in children}
                    for future in concurrent.futures.as_completed(future_to_child):
                        child = future_to_child[future]
                        try:
                            indiv = future.result()
                            modChildren.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local: %s' % (str(child), exc))

                # split
                cluster = SplitDepots.splitByDepot(modChildren[0])
                # print(cluster)
                individual1 = split.splitLinear(cluster)
                cluster = SplitDepots.splitByDepot(modChildren[1])
                # print(cluster)
                individual2 = split.splitLinear(cluster)

                individuals = [individual1, individual2]

                # print("individual: ")
                # print(individual1)
                # print(individual2)
                for a in range(2):
                    for ii, c1 in enumerate(individuals[a].get_giantTour()):
                        for jj, c2 in enumerate(individuals[a].get_giantTour()):
                            if ii != jj and c1 == c2:
                                print("Elementos iguais na mutação")
                                exit(1)

                # Busca Local
                
                # duas threads
                ini = time.time()
                modIndividuals =  []
                LS = ls()
                # modIndividuals.append(LS.LS(individuals[0]))
                # modIndividuals.append(LS.LS(individuals[1]))
                with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
                    future_to_individual = {executor.submit(LS.LS,ind,nMovimentations='random' ,where='ls'): ind for ind in individuals}
                    for future in concurrent.futures.as_completed(future_to_individual):
                        ind = future_to_individual[future]
                        try:
                            indiv = future.result()
                            modIndividuals.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local: %s' % (str(ind), exc))
                            traceback.print_exc()
                # print(future_to_individual)
                # print(individuals[0])
                # print(modIndividuals)
                # exit(1)
                tTotal = (time.time() - ini)/60
                tLS += tTotal
                for a in range(2):
                    for e1, c1 in enumerate(modIndividuals[a].get_giantTour()):
                        for e2, c2 in enumerate(modIndividuals[a].get_giantTour()):
                            if e1 != e2 and c1 == c2:
                                print("Elementos iguais na busca local")
                                exit(1)
                # exit(1)
                # avalie a população
                for a in range(2):
                    # indivíduo diferente do resto da população
                    if self.is_different(modIndividuals[a],descendant):
                        #pop.addIndividual(modIndividuals[a])
                        descendant.append(modIndividuals[a])

                # pop.sortPopulation()
                # population = pop.get_population()
            # inserir descendentes à população
            for desc in descendant:
                if pop.is_different(desc):
                    pop.addIndividual(desc)
            
            # inserir indivíduos da lista newIndividuals (se existir) à população

            #início seção crítica
            mutex.acquire()
            # print("verificar lista")
            # print(newIndividuals)
            if newIndividuals:
                # print("novo: "+ str(len(newIndividuals)))
                for ni in newIndividuals:
                    if pop.is_different(ni):
                        # print("achou assíncrona")
                        pop.addIndividual(ni)
                newIndividuals = []
            mutex.release()
            #fim seção crítica
            
            pop.sortPopulation()
            population = pop.get_population()
            # promoção

            p = max(round(config.SIZE_POP * 0.1),1) #10% da população
            LSBetter = ls()
         
            modIndividuals =  []
            individuals = []
            selProbalities = pop.get_selProbabilities() # probabilidade de seleção
            individuals = np.random.choice(population,p,replace=False,p=selProbalities)
            individuals = np.append(individuals, pop.showBestSolution())
            with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
                    future_to_individual = {executor.submit(LSBetter.LS,ind,where='ls'): ind for ind in individuals}
                    for future in concurrent.futures.as_completed(future_to_individual):
                        ind = future_to_individual[future]
                        try:
                            indiv = future.result()
                            modIndividuals.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local - promoção: %s' % (str(ind), exc))
                            traceback.print_exc()
            #exit(1)
            # avalie a população
            for a in modIndividuals:

                for e1, c1 in enumerate(a.get_giantTour()):
                        for e2, c2 in enumerate(a.get_giantTour()):
                            if e1 != e2 and c1 == c2:
                                print("Elementos iguais na busca local - promoção")
                                exit(1)

                # indivíduo diferente do resto da população
                if pop.is_different(a):
                    pop.addIndividual(a)

            tTotalP = (time.time() - ini)/60

            pop.sortPopulation()

            # defina a população sobrevivente
            best = pop.defineSurvivors(config.SIZE_POP)
            population = pop.get_population()
            
            #busca local exaustiva - best improvemment
            # p = 3 # 3 threads
            individuals = []
            # selProbalities = pop.get_selProbabilities() # probabilidade de seleção
            # individuals = np.random.choice(population,(p-1),replace=False,p=selProbalities)
            individuals = np.append(individuals, pop.showBestSolution())
            individuals = np.append(individuals, pop.showSecondBestSolution())
            
            # cria threads 
            for individual in individuals:
                if np.random.random() < config.PROB_LS_BEST:
                    if th.active_count()<4: # máximo 3 threads agindo de forma assíncrona
                        a = MyThread(individual) #inicializa thread
                        a.start()
                        threads.append(a)
            
            if round(bestPrev,9) == round(best,9):
                cont += 1
            else:
                cont = 0
            # if sumControl > config.CONT_METRIC:
            #     # idum = i * seed
            #     population = pop.changePopulation()
            #     sumControl = 0
            if cont > config.GEN_NO_EVOL:
                # population = pop.changePopulation()
                # cont = 0
                print("ALERTA POPULAÇÃO PAROU DE EVOLUIR")
            pop.sortPopulation()
            population = pop.get_population()
            tAll = (time.time() - tAllIni)/60
            timeControl += tAll

            print("GERAÇÃO: {} - Custo: {} - Tempo LS: {} - Tempo LS Promotion: {} - Tempo Total: {}".format(i,
                                                   pop.showBestSolution().get_cost(),tLS,tTotalP,tAll))

            i += 1


        print("th.active_count(): "+str(th.active_count()))
        for t in threads:
            t.join()
        if newIndividuals:
            for ni in newIndividuals:
                if pop.is_different(ni):
                    pop.addIndividual(ni)
        newIndividuals = []
        pop.sortPopulation()
        return pop.showBestSolution()
Пример #6
0
    def GA(self):
        # define população inicial

        pop = Population()
        population = pop.definePopulation(config.MI)

        def minor(x, y):
            return x if x.get_cost() < y.get_cost() else y

        best = 0
        bestPrev = 0
        controlPop = True
        controlPopPrev = True
        sumControl = 0
        cont = 0
        # avalie a população

        # critério de parada
        i = 0
        while i < config.GEN and cont <= config.GEN_NO_EVOL:
            tAllIni = time.time()
            bestPrev = best
            controlPopPrev = controlPop
            tLS = 0

            #sizePopulation = len(population)
            for j in range(round(config.LAMBDA / 2)):
                selProbalities = pop.get_selProbabilities(
                )  # probabilidade de seleção
                # print("pop: "+str(len(population)))
                # print("prob: "+str(len(selProbalities)))
                # selecione os pais
                aux = np.random.choice(population,
                                       2,
                                       replace=False,
                                       p=selProbalities)

                # aux1 = population[np.random.randint(len(population))]
                # aux2 = population[np.random.randint(len(population))]

                P1 = minor(aux[0], aux[1])

                aux = np.random.choice(population,
                                       2,
                                       replace=False,
                                       p=selProbalities)
                # aux1 = population[np.random.randint(len(population))]
                # aux2 = population[np.random.randint(len(population))]
                P2 = minor(aux[0], aux[1])

                # Crossover

                rand = 0.5  #np.random.random()
                # print(rand)
                # print(P1)
                # print(P2)
                children = []
                if rand > 0.5:
                    children = cross.OBX(copy.deepcopy(P1), copy.deepcopy(P2))
                else:
                    children = cross.PMX(copy.deepcopy(P1), copy.deepcopy(P2))
                # print("child: \n")
                # print(child)
                # for a in range(2):
                #     for e1, c1 in enumerate(children[a]):
                #         for e2, c2 in enumerate(children[a]):
                #             if e1 != e2 and c1 == c2:
                #                 print("Elementos iguais")
                #                 exit(1)

                # Mutação

                # duas threads
                modChildren = []
                with concurrent.futures.ThreadPoolExecutor(
                        max_workers=2) as executor:
                    future_to_child = {
                        executor.submit(Mutation.mutation, child): child
                        for child in children
                    }
                    for future in concurrent.futures.as_completed(
                            future_to_child):
                        child = future_to_child[future]
                        try:
                            indiv = future.result()
                            modChildren.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local: %s' %
                                  (str(child), exc))

                # split
                cluster = SplitDepots.splitByDepot(modChildren[0])
                # print(cluster)
                individual1 = split.splitLinear(cluster)
                cluster = SplitDepots.splitByDepot(modChildren[1])
                # print(cluster)
                individual2 = split.splitLinear(cluster)

                individuals = [individual1, individual2]

                # print("individual: ")
                # print(individual1)
                # print(individual2)
                for a in range(2):
                    for ii, c1 in enumerate(individuals[a].get_giantTour()):
                        for jj, c2 in enumerate(
                                individuals[a].get_giantTour()):
                            if ii != jj and c1 == c2:
                                print("Elementos iguais na mutação")
                                exit(1)

                # Busca Local

                # duas threads
                ini = time.time()
                modIndividuals = []
                LS = ls()
                # modIndividuals.append(LS.LS(individuals[0]))
                # modIndividuals.append(LS.LS(individuals[1]))
                with concurrent.futures.ThreadPoolExecutor(
                        max_workers=2) as executor:
                    future_to_individual = {
                        executor.submit(LS.LS,
                                        ind,
                                        nMovimentations='random',
                                        where='ls'): ind
                        for ind in individuals
                    }
                    for future in concurrent.futures.as_completed(
                            future_to_individual):
                        ind = future_to_individual[future]
                        try:
                            indiv = future.result()
                            modIndividuals.append(indiv)
                        except Exception as exc:
                            print('%s gerou uma exceção na busca local: %s' %
                                  (str(ind), exc))
                # print(future_to_individual)
                # print(individuals[0])
                # print(modIndividuals)
                # exit(1)
                tTotal = (time.time() - ini) / 60
                tLS += tTotal
                for a in range(2):
                    for e1, c1 in enumerate(modIndividuals[a].get_giantTour()):
                        for e2, c2 in enumerate(
                                modIndividuals[a].get_giantTour()):
                            if e1 != e2 and c1 == c2:
                                print("Elementos iguais na busca local")
                                exit(1)
                # exit(1)
                # avalie a população
                for a in range(2):
                    # indivíduo diferente do resto da população
                    if pop.is_different(modIndividuals[a]):
                        pop.addIndividual(modIndividuals[a])

                pop.sortPopulation()
                population = pop.get_population()

            # promoção

            p = max(round(config.LAMBDA * 0.1), 2)  #10% da população
            ini = time.time()
            LSBest = ls()
            # if np.random.random() < config.PROB_LS:
            #     bestIndividual = LSBest.LS(population[0])
            #     if pop.is_different(bestIndividual):
            #         pop.addIndividual(bestIndividual)
            #         population = pop.get_population()
            modIndividuals = []
            individuals = []
            individuals = np.random.choice(population, p - 1, replace=False)
            individuals = np.append(individuals, pop.showBestSoution())
            with concurrent.futures.ThreadPoolExecutor(
                    max_workers=4) as executor:
                future_to_individual = {
                    executor.submit(LSBest.LS, ind, where='ls'): ind
                    for ind in individuals
                }
                for future in concurrent.futures.as_completed(
                        future_to_individual):
                    ind = future_to_individual[future]
                    try:
                        indiv = future.result()
                        modIndividuals.append(indiv)
                    except Exception as exc:
                        print('%s gerou uma exceção na busca local: %s' %
                              (str(ind), exc))

            # avalie a população
            for a in modIndividuals:

                for e1, c1 in enumerate(a.get_giantTour()):
                    for e2, c2 in enumerate(a.get_giantTour()):
                        if e1 != e2 and c1 == c2:
                            print("Elementos iguais na busca local - promoção")
                            exit(1)

                # indivíduo diferente do resto da população
                if pop.is_different(a):
                    pop.addIndividual(a)

            tTotalP = (time.time() - ini) / 60

            pop.sortPopulation()

            # defina a população sobrevivente
            best = pop.defineSurvivors(config.MI)

            # verifica se houve evolução na população
            # if not pop.verifyDiversity():
            #     #print("Baixa diversidade")

            #     controlPop = False
            # if not controlPop and controlPop == controlPopPrev:
            #     sumControl += 1
            # else:
            #     sumControl = 0
            if bestPrev == best:
                cont += 1
            else:
                cont = 0
            # if sumControl > config.CONT_METRIC:
            #     population = pop.changePopulation()
            #     sumControl = 0
            if cont > config.GEN_NO_EVOL:
                print("ALERTA POPULAÇÃO PAROU DE EVOLUIR")

            population = pop.get_population()
            tAll = (time.time() - tAllIni) / 60

            print(
                "GERAÇÃO: {} - Custo: {} - Tempo LS: {} - Tempo LS Promotion: {} - Tempo Total: {}"
                .format(i,
                        pop.showBestSoution().get_cost(), tLS, tTotalP, tAll))

            i += 1

        # liste os melhores indivíduos
        print(population)
        print(len(population))