pso_optimize.py

def optimize(self):
    for i in range(self.numOfEpochs):

        for j in range(len(self.swarm_list)):

            current_particle = self.swarm_list[j]  # get current particle

            Vcurr = grad_error(current_particle.position)  # calculate current velocity of the particle

            deltaV = self.w * Vcurr \
                     + self.c1 * (current_particle.best_part_pos - current_particle.position) \
                     + self.c2 * (self.best_swarm_position - current_particle.position)  # calculate delta V

            new_position = self.swarm_list[j].position - self.lr * deltaV  # calculate the new position

            self.swarm_list[j].setPos(new_position)  # update the position of particle

            if error(new_position) < self.best_swarm_error:  # check the position if it's best for swarm
                self.best_swarm_position = new_position
                self.best_swarm_error = error(new_position)

        print('Epoch: {0} | Best position: [{1},{2}] | Best known error: {3}'.format(i,
                                                                                     self.best_swarm_position[0],
                                                                                     self.best_swarm_position[1],
                                                                                     self.best_swarm_error))