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@@ 739-769 (lines=31) @@
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| 736 |
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d.update({'nmutt': self.nmutt}) |
| 737 |
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return d |
| 738 |
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| 739 |
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def runIteration(self, task, pop, fpop, xb, fxb, **dparams): |
| 740 |
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r"""Core function of algorithm. |
| 741 |
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| 742 |
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Args: |
| 743 |
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task (Task): Optimization task. |
| 744 |
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pop (numpy.ndarray): Current population of particles. |
| 745 |
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fpop (numpy.ndarray): Current particles function/fitness values. |
| 746 |
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xb (numpy.ndarray): Current global best particle. |
| 747 |
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fxb (float: Current global best particles function/fitness value. |
| 748 |
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**dparams: Additional arguments. |
| 749 |
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| 750 |
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Returns: |
| 751 |
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Tuple[np.ndarray, np.ndarray, np.ndarray, float, dict]: |
| 752 |
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1. New population of particles. |
| 753 |
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2. New populations function/fitness values. |
| 754 |
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3. New global best particle. |
| 755 |
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4. New global best particle function/fitness value. |
| 756 |
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5. Additional arguments. |
| 757 |
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| 758 |
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See Also: |
| 759 |
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* :func:`NiaPy.algorithm.basic.WeightedVelocityClampingParticleSwarmAlgorithm.runIteration` |
| 760 |
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""" |
| 761 |
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pop, fpop, xb, fxb, d = CenterParticleSwarmOptimization.runIteration(self, task, pop, fpop, xb, fxb, **dparams) |
| 762 |
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v = d['V'] |
| 763 |
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v_a = (np.sum(v, axis=0) / len(v)) |
| 764 |
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v_a = v_a / np.max(np.abs(v_a)) |
| 765 |
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for _ in range(self.nmutt): |
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g = task.repair(xb + v_a * self.uniform(task.Lower, task.Upper), self.Rand) |
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fg = task.eval(g) |
| 768 |
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if fg <= fxb: xb, fxb = g, fg |
| 769 |
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return pop, fpop, xb, fxb, d |
| 770 |
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| 771 |
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class MutatedCenterUnifiedParticleSwarmOptimization(MutatedCenterParticleSwarmOptimization): |
| 772 |
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r"""Implementation of Mutated Particle Swarm Optimization. |
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@@ 642-672 (lines=31) @@
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| 639 |
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d.update({'nmutt': self.nmutt}) |
| 640 |
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return d |
| 641 |
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| 642 |
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def runIteration(self, task, pop, fpop, xb, fxb, **dparams): |
| 643 |
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r"""Core function of algorithm. |
| 644 |
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| 645 |
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Args: |
| 646 |
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task (Task): Optimization task. |
| 647 |
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pop (numpy.ndarray): Current population of particles. |
| 648 |
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fpop (numpy.ndarray): Current particles function/fitness values. |
| 649 |
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xb (numpy.ndarray): Current global best particle. |
| 650 |
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fxb (float): Current global best particles function/fitness value. |
| 651 |
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**dparams: Additional arguments. |
| 652 |
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| 653 |
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Returns: |
| 654 |
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Tuple[np.ndarray, np.ndarray, np.ndarray, float, dict]: |
| 655 |
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1. New population of particles. |
| 656 |
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2. New populations function/fitness values. |
| 657 |
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3. New global best particle. |
| 658 |
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4. New global best particle function/fitness value. |
| 659 |
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5. Additional arguments. |
| 660 |
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| 661 |
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See Also: |
| 662 |
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* :func:`NiaPy.algorithm.basic.WeightedVelocityClampingParticleSwarmAlgorithm.runIteration` |
| 663 |
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""" |
| 664 |
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pop, fpop, xb, fxb, d = ParticleSwarmAlgorithm.runIteration(self, task, pop, fpop, xb, fxb, **dparams) |
| 665 |
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v = d['V'] |
| 666 |
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v_a = (np.sum(v, axis=0) / len(v)) |
| 667 |
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v_a = v_a / np.max(np.abs(v_a)) |
| 668 |
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for _ in range(self.nmutt): |
| 669 |
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g = task.repair(xb + v_a * self.uniform(task.Lower, task.Upper), self.Rand) |
| 670 |
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fg = task.eval(g) |
| 671 |
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if fg <= fxb: xb, fxb = g, fg |
| 672 |
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return pop, fpop, xb, fxb, d |
| 673 |
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| 674 |
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class MutatedCenterParticleSwarmOptimization(CenterParticleSwarmOptimization): |
| 675 |
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r"""Implementation of Mutated Particle Swarm Optimization. |
NiaOrg /
NiaPy
