ParticleSwarm.py 文件源码

def run(self, verbose=True):
        """
        Conducts particle swarm optimization

        :param verbose: indicates whether or not to print progress regularly
        :return: best member of swarm and objective function value of best member of swarm
        """
        self._clear()
        for i in range(self.max_steps):
            self.cur_steps += 1

            if verbose and ((i + 1) % 100 == 0):
                print(self)

            u1 = zeros((self.swarm_size, self.swarm_size))
            u1[diag_indices_from(u1)] = [random() for x in range(self.swarm_size)]
            u2 = zeros((self.swarm_size, self.swarm_size))
            u2[diag_indices_from(u2)] = [random() for x in range(self.swarm_size)]

            vel_new = (self.c1 * self.vel) + \
                      (self.c2 * dot(u1, (self.best - self.pos))) + \
                      (self.c3 * dot(u2, (self.global_best - self.pos)))

            pos_new = self.pos + vel_new

            self._best(self.pos, pos_new)
            self.pos = pos_new
            self.scores = self._score(self.pos)
            self._global_best()

            if self._objective(self.global_best[0]) < (self.min_objective or 0):
                print("TERMINATING - REACHED MINIMUM OBJECTIVE")
                return self.global_best[0], self._objective(self.global_best[0])
        print("TERMINATING - REACHED MAXIMUM STEPS")
        return self.global_best[0], self._objective(self.global_best[0])