NiaOrg /
NiaPy
| @@ 116-138 (lines=23) @@ | ||
| 113 | }) |
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| 114 | return d |
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| 115 | ||
| 116 | def initPopulation(self, task): |
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| 117 | r"""Initialize the starting population. |
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| 118 | ||
| 119 | Parameters: |
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| 120 | task (Task): Optimization task |
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| 121 | ||
| 122 | Returns: |
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| 123 | Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]: |
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| 124 | 1. New population. |
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| 125 | 2. New population fitness/function values. |
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| 126 | 3. Additional arguments: |
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| 127 | * A (float): Loudness. |
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| 128 | * S (numpy.ndarray): TODO |
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| 129 | * Q (numpy.ndarray[float]): TODO |
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| 130 | * v (numpy.ndarray[float]): TODO |
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| 131 | ||
| 132 | See Also: |
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| 133 | * :func:`NiaPy.algorithms.Algorithm.initPopulation` |
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| 134 | """ |
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| 135 | Sol, Fitness, d = Algorithm.initPopulation(self, task) |
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| 136 | A, S, Q, v = np.full(self.NP, self.A), np.full([self.NP, task.D], 0.0), np.full(self.NP, 0.0), np.full([self.NP, task.D], 0.0) |
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| 137 | d.update({'A': A, 'S': S, 'Q': Q, 'v': v}) |
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| 138 | return Sol, Fitness, d |
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| 139 | ||
| 140 | def localSearch(self, best, A, task, **kwargs): |
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| 141 | r"""Improve the best solution according to the Yang (2010). |
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| @@ 109-130 (lines=22) @@ | ||
| 106 | }) |
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| 107 | return d |
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| 108 | ||
| 109 | def initPopulation(self, task): |
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| 110 | r"""Initialize the starting population. |
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| 111 | ||
| 112 | Parameters: |
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| 113 | task (Task): Optimization task |
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| 114 | ||
| 115 | Returns: |
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| 116 | Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]: |
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| 117 | 1. New population. |
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| 118 | 2. New population fitness/function values. |
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| 119 | 3. Additional arguments: |
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| 120 | * S (numpy.ndarray): Solutions |
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| 121 | * Q (numpy.ndarray[float]): Frequencies |
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| 122 | * v (numpy.ndarray[float]): Velocities |
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| 123 | ||
| 124 | See Also: |
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| 125 | * :func:`NiaPy.algorithms.Algorithm.initPopulation` |
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| 126 | """ |
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| 127 | Sol, Fitness, d = Algorithm.initPopulation(self, task) |
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| 128 | S, Q, v = full([self.NP, task.D], 0.0), full(self.NP, 0.0), full([self.NP, task.D], 0.0) |
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| 129 | d.update({'S': S, 'Q': Q, 'v': v}) |
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| 130 | return Sol, Fitness, d |
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| 131 | ||
| 132 | def localSearch(self, best, task, **kwargs): |
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| 133 | r"""Improve the best solution according to the Yang (2010). |
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| @@ 65-86 (lines=22) @@ | ||
| 62 | Algorithm.setParameters(self, NP=80, **ukwargs) |
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| 63 | self.A, self.r = 0.9, 0.1 |
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| 64 | ||
| 65 | def initPopulation(self, task): |
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| 66 | r"""Initialize the initial population. |
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| 67 | ||
| 68 | Parameters: |
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| 69 | task (Task): Optimization task |
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| 70 | ||
| 71 | Returns: |
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| 72 | Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]: |
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| 73 | 1. New population. |
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| 74 | 2. New population fitness/function values. |
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| 75 | 3. Additional arguments: |
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| 76 | * S (numpy.ndarray): Solutions |
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| 77 | * Q (numpy.ndarray[float]): Frequencies |
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| 78 | * v (numpy.ndarray[float]): Velocities |
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| 79 | ||
| 80 | See Also: |
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| 81 | * :func:`NiaPy.algorithms.Algorithm.initPopulation` |
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| 82 | """ |
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| 83 | Sol, Fitness, d = Algorithm.initPopulation(self, task) |
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| 84 | S, Q, v = full([self.NP, task.D], 0.0), full(self.NP, 0.0), full([self.NP, task.D], 0.0) |
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| 85 | d.update({'S': S, 'Q': Q, 'v': v}) |
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| 86 | return Sol, Fitness, d |
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| 87 | ||
| 88 | def localSearch(self, best, task, **kwargs): |
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| 89 | r"""Improve the best solution according to the Yang (2010). |
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