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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import random |
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import numpy as np |
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from ...base_optimizer import BaseOptimizer |
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from ...base_positioner import BasePositioner |
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class ParticleSwarmOptimizer(BaseOptimizer): |
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def __init__(self, _opt_args_): |
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super().__init__(_opt_args_) |
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self.n_positioners = self._opt_args_.n_particles |
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def _init_particle(self, _cand_): |
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_p_ = Particle() |
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_p_.pos_new = _cand_._space_.get_random_pos() |
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_p_.velo = np.zeros(len(_cand_._space_.search_space)) |
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self._optimizer_eval(_cand_, _p_) |
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self._update_pos(_cand_, _p_) |
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return _p_ |
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def _move_positioner(self, _cand_, _p_): |
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r1, r2 = random.random(), random.random() |
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A = self._opt_args_.inertia * _p_.velo |
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B = ( |
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self._opt_args_.cognitive_weight |
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* r1 |
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* np.subtract(_p_.pos_best, _p_.pos_new) |
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) |
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C = ( |
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self._opt_args_.social_weight |
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* r2 |
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* np.subtract(_cand_.pos_best, _p_.pos_new) |
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) |
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new_velocity = A + B + C |
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_p_.velo = new_velocity |
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_p_.move_part(_cand_, _p_.pos_new) |
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def _iterate(self, i, _cand_): |
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_p_current = self.p_list[i % self.n_positioners] |
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self._move_positioner(_cand_, _p_current) |
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self._optimizer_eval(_cand_, _p_current) |
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self._update_pos(_cand_, _p_current) |
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return _cand_ |
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def _init_iteration(self, _cand_): |
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p = self._init_particle(_cand_) |
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self._optimizer_eval(_cand_, p) |
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self._update_pos(_cand_, p) |
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return p |
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class Particle(BasePositioner): |
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def __init__(self): |
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super().__init__(self) |
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self.velo = None |
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def move_part(self, _cand_, pos): |
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pos_new = (pos + self.velo).astype(int) |
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# limit movement |
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n_zeros = [0] * len(_cand_._space_.dim) |
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self.pos_new = np.clip(pos_new, n_zeros, _cand_._space_.dim) |
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SimonBlanke /
Hyperactive
