def init_population(self):
for _ in range(self.population_number):
size = self.cloudlet_num
p = DParticle(size)
p.solution = np.random.randint(0,
self.machine_number,
size=self.cloudlet_num)
p.velocity = -5 + 10 * np.random.rand(self.cloudlet_num)
p.fitness = calculate_fitness(p.solution, self.cloudlets, self.vms)
self.particles.append(p)
def update_velocity(self, p: DParticle, lb: DParticle, gb: DParticle):
E1 = np.array([np.random.random() for _ in range(self.cloudlet_num)
]) # [0.1, 0.2, 0.002, 0.4, ...]
E2 = np.array([np.random.random() for _ in range(self.cloudlet_num)])
v1 = np.array(gb.solution) - np.array(p.solution)
v2 = np.array(lb.solution) - np.array(p.solution)
velocity = self.c1 * E1 * v1 + self.c2 * E2 * v2
velocity = np.clip(velocity, -self.vmax, self.vmax)
p.velocity = p.velocity * self.w + velocity
def getSeries(self, v0):
tempSet = [[0] * self.cloudlet_num] * 30
tempSet[0] = [x / self.machine_number for x in v0]
for i in range(1, 30):
tempSet[i] = [x for x in logistic_function(tempSet[i - 1], 3.5)]
for i in range(30):
tempSet[i] = [int(self.machine_number * x) for x in tempSet[i]]
# print(tempSet)
tempFit = []
for i in range(30):
tempFit.append(
calculate_fitness(tempSet[i], self.cloudlets, self.vms))
best_score = 0
best_solution = None
for i in range(self.chaosNum):
if tempFit[i] > best_score:
best_score = tempFit[i]
best_solution = tempSet[i]
res = DParticle(self.cloudlet_num)
res.velocity = self.gbest.velocity
res.solution = best_solution
res.fitness = best_score
return res
def update_position(self, p: DParticle):
p.solution = np.abs(p.solution + p.velocity)
for t in range(len(self.cloudlets)):
if p.solution[t] > self.machine_number:
p.solution[t] = np.ceil(p.solution[t]) % self.machine_number
p.solution = list(map(int, p.solution))